Published for SISSA by Springer Received: January 1, 2019 Revised: March 27, 2019 Accepted: April 22, 2019 Published: May 21, 2019 Search for top-quark decays t → Hq with 36 fb−1 of √ pp collision data at s = 13 TeV with the ATLAS JHEP05(2019)123 detector The ATLAS collaboration E-mail: atlas.publications@cern.ch Abstract: A search for flavour-changing neutral current decays of a top quark into an up-type quark (q = u, c) and the Standard Model Higgs boson, t → Hq, is presented. The √ search is based on a dataset of pp collisions at s = 13 TeV recorded in 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider and corresponding to an integrated luminosity of 36.1 fb−1 . Two complementary analyses are performed to search for top-quark pair events in which one top quark decays into W b and the other top quark decays into Hq, and target the H → bb̄ and H → τ + τ − decay modes, respectively. The high multiplicity of b-quark jets, or the presence of hadronically decaying τ -leptons, is exploited in the two analyses respectively. Multivariate techniques are used to separate the signal from the background, which is dominated by top-quark pair production. No significant excess of events above the background expectation is found, and 95% CL upper limits on the t → Hq branching ratios are derived. The combination of these searches with ATLAS searches in diphoton and multilepton final states yields observed (expected) 95% CL upper limits on the t → Hc and t → Hu branching ratios of 1.1 × 10−3 (8.3 × 10−4 ) and 1.2 × 10−3 (8.3 × 10−4 ), respectively. The corresponding combined observed (expected) upper limits on the |λtcH | and |λtuH | couplings are 0.064 (0.055) and 0.066 (0.055), respectively. Keywords: Hadron-Hadron scattering (experiments), Rare decay, Top physics ArXiv ePrint: 1812.11568 Open Access, Copyright CERN, https://doi.org/10.1007/JHEP05(2019)123 for the benefit of the ATLAS Collaboration. Article funded by SCOAP3 . Contents 1 Introduction 2 2 ATLAS detector 3 3 Event reconstruction 4 4 Data sample and event preselection 7 JHEP05(2019)123 5 Signal and background modelling 8 5.1 Simulated signal and background processes 8 5.2 Backgrounds with fake leptons 10 5.2.1 Fake electrons and muons 10 5.2.2 Fake τ -lepton candidates 11 6 Strategy for the tqH(bb̄) search 11 6.1 Event categorisation 11 6.2 Likelihood discriminant 12 7 Strategy for the tqH(τ τ ) search 14 7.1 Event categorisation and kinematic reconstruction 14 7.2 Multivariate discriminant 17 8 Systematic uncertainties 19 8.1 Luminosity 19 8.2 Reconstructed objects 19 8.3 Background modelling 23 8.4 Signal modelling 25 9 Statistical analysis 26 10 Results 27 10.1 tqH(bb̄) search 27 10.2 tqH(τ τ ) search 28 10.3 Combination of ATLAS searches 31 11 Conclusion 35 A Pre-fit and post-fit event yields in the tqH(bb̄) search 38 B Pre-fit and post-fit event yields in the tqH(τ τ ) search 41 The ATLAS collaboration 50 –1– 1 Introduction Following the observation of the Higgs boson by the ATLAS and CMS experiments [1, 2] at the Large Hadron Collider (LHC), a comprehensive programme of measurements of its properties is underway. An interesting possibility is the presence of flavour-changing neutral-current (FCNC) interactions between the Higgs boson, the top quark, and a u- or c-quark, tqH (q = u, c). Since the Higgs boson is lighter than the top quark [3], such interactions would manifest themselves as FCNC top-quark decays [4], t → Hq. In the Standard Model (SM), such decays are suppressed relative to the dominant t → W b decay mode, since tqH interactions are forbidden at the tree level and suppressed even at JHEP05(2019)123 higher orders in the perturbative expansion due to the Glashow-Iliopoulos-Maiani (GIM) mechanism [5]. As a result, the SM predictions for the t → Hq branching ratios (B) are exceedingly small, B(t → Hu) ∼ 10−17 and B(t → Hc) ∼ 10−15 [6–9], making them undetectable in the foreseeable future. In contrast, large enhancements of these branching ratios are possible in some scenarios beyond the SM. Examples include quark- singlet models [10], two-Higgs-doublet models (2HDM) of type I, with explicit flavour conservation, and of type II, such as the minimal supersymmetric SM (MSSM) [11–14], supersymmetric models with R-parity violation [15], composite Higgs models with partial compositeness [16], or warped extra dimensions models with SM fermions in the bulk [17]. In these scenarios, branching ratios can be as high as B(t → Hq) ∼ 10−5 . An even larger branching ratio of B(t → Hc) ∼ 10−3 can be reached in 2HDM without explicit flavour conservation (type III), since a tree-level FCNC coupling is not forbidden by any symmetry [18–25]. While other FCNC top couplings (tqγ, tqZ, tqg) are also enhanced in these scenarios beyond the SM, the largest enhancements are typically found for the tqH couplings, and in particular the tcH coupling [4]. Searches for t → Hq decays have been performed by the ATLAS and CMS collabo- rations, taking advantage of the large samples of top-quark pair (tt̄) events collected in √ proton-proton (pp) collisions at centre-of-mass energies of s = 7 TeV and 8 TeV [26–28] √ during Run 1 of the LHC, as well as at s = 13 TeV [29–31] using early Run 2 data. In these searches, one of the top quarks is required to decay into W b, while the other top quark decays into Hq, yielding tt̄ → W bHq.1 The Higgs boson is assumed to have a mass of mH = 125 GeV and to decay as predicted by the SM. The simplifying assump- tion of SM-like Higgs boson branching ratios is motivated by the fact that measurements of the flavour-diagonal Higgs boson couplings by the ATLAS and CMS collaborations are in agreement with the SM prediction within about 10% [32, 33]. Furthermore, typical beyond-the-SM scenarios that predict significant enhancements to B(t → Hq), also predict modifications to the Higgs boson branching ratios at the few percent level or below, well be- yond the current experimental precision. Some of the most sensitive single-channel searches have been performed in the H → γγ decay mode, which has a small branching ratio of B(H → γγ) ' 0.2%, but benefits from having a very small background contamination and excellent diphoton mass resolution. Searches targeting signatures with two same-charge In the following, W bHq is used to denote both W + bH q̄ and its charge conjugate, HqW − b̄. Similarly, 1 W bW b is used to denote W + bW − b̄. –2– leptons or three leptons (electrons or muons), generically referred to as multileptons, are able to exploit a branching ratio that is significantly larger for the H → W W ∗ , τ τ decay modes than for the H → γγ decay mode, and are also characterised by relatively small back- grounds. Finally, searches have also been performed exploiting the dominant Higgs boson decay mode, H → bb̄, which has a branching ratio of B(H → bb̄) ' 58%. Compared with √ Run 1, the Run 2 searches benefit from the increased tt̄ cross section at s = 13 TeV, as well √ as the larger integrated luminosity. Using 36.1 fb−1 of data at s = 13 TeV, the ATLAS Collaboration has derived upper limits at 95% confidence level (CL) of B(t → Hc) < 0.22% using H → γγ decays [29], and of B(t → Hc) < 0.16% based on multilepton signatures resulting from H → W W ∗ , H → τ + τ − in which both τ -leptons decay leptonically, or JHEP05(2019)123 H → ZZ ∗ [30]. These upper limits are derived assuming that B(t → Hu) = 0. Similar upper limits are obtained for B(t → Hu) if B(t → Hc) = 0. The CMS Collaboration √ has performed a search using H → bb̄ decays [31] with 35.9 fb−1 of data at s = 13 TeV, resulting in upper limits of B(t → Hc) < 0.47% and B(t → Hu) < 0.47%, in each case neglecting the other decay mode. Compared with previous searches, the search in ref. [31] considers in addition the contribution to the signal from pp → tH production [34]. The searches presented in this paper are focussed on fermionic decay modes of the Higgs boson. Therefore, they help to complete the ATLAS experiment’s programme of searches √ for t → Hq decays based on pp collision data at s = 13 TeV recorded in 2015 and 2016. The corresponding integrated luminosity is 36.1 fb−1 . Two analyses are performed, searching for tt̄ → W bHq production (ignoring pp → tH production) and targeting the H → bb̄ and H → τ + τ − decay modes, which this paper refers to as “tqH(bb̄) search” and “tqH(τ τ ) search”, respectively. The tqH(bb̄) search selects events with one isolated electron or muon from the W → `ν decay, and multiple jets, several of which are identified with high purity as originating from the hadronisation of b-quarks. The tqH(τ τ ) search selects events with two τ -lepton candidates, at least one of which decays hadronically, as well as multiple jets. The latter requirement aims to select events with a hadronically decaying W boson, since this allows an improved reconstruction of the event kinematics. Both searches employ multivariate techniques to discriminate between the signal and the background on the basis of their different kinematics. These two searches are combined with previous ATLAS searches in the diphoton and multilepton final states using the same dataset [29, 30], and bounds are set on B(t → Hc) and B(t → Hu), as well as on the corresponding non-flavour-diagonal Yukawa couplings. The combination is performed after verifying the overall consistency of the results obtained by the different searches, which exploit very different experimental signatures and thus are affected by different backgrounds and related systematic uncertainties. By combining all searches, the expected sensitivity is improved by about a factor of two relative to the most sensitive individual results. 2 ATLAS detector The ATLAS detector [35] at the LHC covers almost the entire solid angle around the collision point,2 and consists of an inner tracking detector surrounded by a thin super- 2 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector. The x-axis points from the IP to the centre of the LHC ring, the y-axis –3– conducting solenoid producing a 2 T axial magnetic field, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large toroid magnet assemblies with eight coils each. The inner detector contains a high-granularity silicon pixel detector, including the insertable B-layer [36–38], installed in 2014, and a silicon microstrip tracker, together providing a precise reconstruction of tracks of charged particles in the pseudora- pidity range |η| < 2.5. The inner detector also includes a transition radiation tracker that provides tracking and electron identification for |η| < 2.0. The calorimeter system cov- ers the pseudorapidity range |η| < 4.9. Within the region |η| < 3.2, electromagnetic (EM) calorimetry is provided by barrel and endcap high-granularity lead/liquid-argon (LAr) sam- pling calorimeters, with an additional thin LAr presampler covering |η| < 1.8, to correct for JHEP05(2019)123 energy loss in material upstream of the calorimeters. Hadronic calorimetry is provided by a steel/scintillator-tile calorimeter, segmented into three barrel structures within |η| < 1.7, and two copper/LAr hadronic endcap calorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electro- magnetic and hadronic measurements, respectively. The calorimeters are surrounded by a muon spectrometer within a magnetic field provided by air-core toroid magnets with a bending integral of about 2.5 Tm in the barrel and up to 6 Tm in the endcaps. The muon spectrometer measures the trajectories of muons with |η| < 2.7 using multiple layers of high-precision tracking chambers, and is instrumented with separate trigger chambers covering |η| < 2.4. A two-level trigger system [39], consisting of a hardware-based level-1 trigger followed by a software-based high-level trigger, is used to reduce the event rate to a maximum of around 1 kHz for offline storage. 3 Event reconstruction The event reconstruction is affected by multiple pp collisions in a single bunch crossing and by collisions in neighbouring bunch crossings, referred to as pile-up. Interaction vertices from the pp collisions are reconstructed from at least two tracks with transverse momentum (pT ) larger than 400 MeV that are consistent with originating from the beam collision region in the x–y plane. If more than one primary vertex candidate is found, the candidate whose associated tracks form the largest sum of squared pT [40] is selected as the hard-scatter primary vertex. Electron candidates [41, 42] are reconstructed from energy clusters in the EM calorime- ter that are matched to reconstructed tracks in the inner detector; electron candidates in the transition region between the EM barrel and endcap calorimeters (1.37 < |ηcluster | < 1.52) are excluded. In the tqH(bb̄) (tqH(τ τ )) search, electron candidates are required to have pT > 30 (15) GeV and |ηcluster | < 2.47, and to satisfy tight (medium) likelihood-based iden- tification criteria [41] based on calorimeter, tracking and combined variables that provide separation between electrons and jets. points upward, and the z-axis coincides with the axis of the beam pipe. Cylindrical coordinates (r,φ) are used in the transverse plane, φ being the azimuthal angle around the beam pipe. The pseudorapidity p in terms of the polar angle θ as η = − ln tan(θ/2). Angular distance is measured in units of is defined ∆R ≡ (∆η)2 + (∆φ)2 . –4– Muon candidates [43] are reconstructed by matching track segments in different layers of the muon spectrometer to tracks found in the inner detector; the resulting muon can- didates are re-fitted using the complete track information from both detector systems. In the tqH(bb̄) (tqH(τ τ )) search, muon candidates are required to have pT > 30 (10) GeV and |η| < 2.5 and to satisfy medium identification criteria [43]. Electron (muon) candidates are matched to the primary vertex by requiring that the significance of their transverse impact parameter, d0 , satisfies |d0 /σ(d0 )| < 5 (3), where σ(d0 ) is the measured uncertainty in d0 , and by requiring that their longitudinal impact parameter, z0 , satisfies |z0 sin θ| < 0.5 mm. To further reduce the background from non- JHEP05(2019)123 prompt leptons, photon conversions and hadrons, lepton candidates are also required to be isolated in the tracker and in the calorimeter. A track-based lepton isolation criterion is P trk defined by calculating the quantity IR = pT , where the scalar sum includes all tracks (excluding the lepton candidate itself) within the cone defined by ∆R < Rcut around the direction of the lepton. The value of Rcut is the smaller of rmin and 10 GeV/p`T , where rmin is set to 0.2 (0.3) for electron (muon) candidates, and p`T is the lepton pT . The tqH(bb̄) search requires lepton candidates to satisfy IR /p`T < 0.06, while the tqH(τ τ ) search makes pT -dependent requirements on IR /p`T . Additionally, the tqH(τ τ ) search requires leptons to satisfy a calorimeter-based isolation criterion: the sum of the transverse energy within a cone of size ∆R < 0.2 around the lepton, after subtracting the contributions from pile-up and the energy deposit of the lepton itself, is required to be less than a pT -dependent fraction of the lepton energy. Candidate jets are reconstructed with the anti-kt algorithm [44, 45] with a radius parameter R = 0.4, as implemented in the FastJet package [46]. Jet reconstruction in the calorimeter starts from topological clustering [47] of individual calorimeter cells calibrated to the electromagnetic energy scale. The reconstructed jets are then calibrated to the particle level by the application of a jet energy scale derived from simulation and in √ situ corrections based on s = 13 TeV data [48]. The calibrated jets used in the tqH(bb̄) search are required to have pT > 25 GeV and |η| < 2.5, while the tqH(τ τ ) search uses jets with pT > 30 GeV and |η| < 4.5. Jet four-momenta are corrected for pile-up effects using the jet-area method [49]. Quality criteria are imposed to reject events that contain any jets arising from non- collision sources or detector noise [50]. To reduce the contamination due to jets originating from pile-up interactions, additional requirements are imposed on the jet vertex tagger (JVT) [51] output for jets with pT < 60 GeV and |η| < 2.4, or on the forward JVT [52] output for jets with pT < 50 GeV and |η| > 2.5. Jets containing b-hadrons are identified (b-tagged) via an algorithm [53, 54] that uses multivariate techniques to combine information about the impact parameters of displaced tracks and the topological properties of secondary and tertiary decay vertices reconstructed within the jet. For each jet, a value for the multivariate b-tagging discriminant is calculated. In the tqH(τ τ ) search, a jet is considered b-tagged if this value is above the threshold corresponding to an average 70% efficiency to tag a b-quark jet, with a light-jet3 rejection 3 Light-jet refers to a jet originating from the hadronisation of a light quark (u, d, s) or a gluon. –5– factor of about 380 and a charm-jet rejection factor of about 12, as determined for jets with pT > 20 GeV and |η| < 2.5 in simulated tt̄ events. In contrast, the tqH(bb̄) search employs a tighter b-tagging requirement, corresponding to an average efficiency of 60% to tag a b- quark jet, and light-jet and charm-jet rejection factors of about 1500 and 34, respectively. Hadronically decaying τ -lepton (τhad ) candidates are reconstructed from energy clus- ters in the calorimeters and associated inner-detector tracks [55]. Candidates are required to have either one or three associated tracks, with a total charge of ±1. Candidates are re- quired to have pT > 25 GeV and |η| < 2.5, excluding the EM calorimeter’s transition region. A boosted decision tree (BDT) discriminant [56–58] using calorimeter- and tracking-based variables is used to identify τhad candidates and reject jet backgrounds. Three working JHEP05(2019)123 points labelled loose, medium and tight are defined, and correspond to different τhad iden- tification efficiency values, with the efficiency designed to be independent of pT . The tqH(τ τ ) search uses the medium working point for the nominal selection, while the loose working point is used for background estimation. The medium working point has a com- bined reconstruction and identification efficiency of 55% (40%) for one-prong (three-prong) τhad decays [59], and an expected rejection factor against light-jets of 100 [55]. Electrons that are reconstructed as one-prong τhad candidates are removed via a BDT trained to reject electrons. Any τhad candidate that is also b-tagged is rejected. Overlaps between reconstructed objects are removed sequentially. In the tqH(bb̄) search, firstly, electron candidates that lie within ∆R = 0.01 of a muon candidate are removed to suppress contributions from muon bremsstrahlung. Overlaps between electron and jet candidates are resolved next, and finally, overlaps between remaining jet candi- dates and muon candidates are removed. Energy clusters from identified electrons are not excluded during jet reconstruction. In order to avoid double-counting of electrons as jets, the closest jet whose axis is within ∆R = 0.2 of an electron is discarded. If the electron is within ∆R = 0.4 of the axis of any jet after this initial removal, the jet is retained and the electron is removed. The overlap removal procedure between the remaining jet candidates and muon candidates is designed to remove those muons that are likely to have arisen in the decay of hadrons and to retain the overlapping jet instead. Jets and muons may also appear in close proximity when the jet results from high-pT muon bremsstrahlung, and in such cases the jet should be removed and the muon retained. Such jets are char- acterised by having very few matching inner-detector tracks. Selected muons that satisfy ∆R(µ, jet) < 0.04 + 10 GeV/pµT are rejected if the jet has at least three tracks originating from the primary vertex; otherwise the jet is removed and the muon is kept. The overlap removal procedure in the tqH(τ τ ) search is similar to that of the tqH(bb̄) search, except that the first step is the removal of τhad candidates within ∆R = 0.2 of electrons or muons, and the last step is the removal of jets whose axis lies within ∆R = 0.2 of the leading (highest-pT ) τhad candidate or the two leading τhad candidates (depending on the search channel). In addition, the muon-jet overlap removal is slightly different: if a muon lies within ∆R = 0.2 of the axis of a jet, the jet is removed if either it has fewer than three tracks originating from the primary vertex or it has a small pT compared with that of the muon (the pT of the jet is less than 50% of the pT of the muon, or the scalar sum of the pT of the tracks associated with the jet is less than 70% of the pT of the muon). –6– The missing transverse momentum p~Tmiss (with magnitude ET miss ) is defined as the negative vector sum of the pT of all selected and calibrated objects in the event, including a term to account for momentum from soft particles in the event which are not associated with any of the selected objects. This soft term is calculated from inner-detector tracks matched to the selected primary vertex to make it more resilient to contamination from pile-up interactions [60]. 4 Data sample and event preselection JHEP05(2019)123 √ Both searches are based on a dataset of pp collisions at s = 13 TeV with 25 ns bunch spacing collected in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb−1 . Only events recorded with a single-electron trigger, a single-muon trigger, or a di-τ trigger under stable beam conditions and for which all detector subsystems were operational are considered. The number of pp interactions per bunch crossing in this dataset ranges from about 8 to 45, with an average of 24. Single-electron and single-muon triggers with low pT thresholds and lepton isolation requirements are combined in a logical OR with higher-threshold triggers but with a looser identification criterion and without any isolation requirement. The lowest pT threshold used for muons is 20 (26) GeV in 2015 (2016), while for electrons the threshold is 24 (26) GeV. For di-τ triggers, the pT threshold of the leading (trailing) τhad candidate is 35 (25) GeV. In both searches, events satisfying the trigger selection are required to have at least one primary vertex candidate. Events selected by the tqH(bb̄) search are recorded with a single-electron or single- muon trigger and are required to have exactly one electron or muon that matches, with ∆R < 0.15, the lepton reconstructed by the trigger. Furthermore, at least four jets are required, of which at least two must be b-tagged. In the tqH(τ τ ) search, events are classified into τlep τhad and τhad τhad channels depend- ing on the multiplicity of selected leptons. Events in the τlep τhad channel are recorded with a single-electron or single-muon trigger and are required to have exactly one selected electron or muon and at least one τhad candidate. The selected electron or muon is required to match, with ∆R < 0.15, the lepton reconstructed by the trigger and to have a pT ex- ceeding the trigger pT threshold by 1 GeV or 2 GeV (depending on the lepton trigger and data-taking conditions). In addition, its electric charge is required to be of opposite sign to that of the leading τhad candidate. Events in the τhad τhad channel are recorded with a di-τ trigger, and are required to have at least two τhad candidates and no selected electrons or muons. The two leading τhad candidates are required to have charges of opposite sign. In addition, in both tqH(τ τ ) search channels, trigger matching for τhad candidates, at least three jets and exactly one b-tagged jet are required. The above requirements apply to the reconstructed objects defined in section 3. These requirements, which ensure a negligible overlap between the tqH(bb̄) and tqH(τ τ ) searches, are referred to as the preselection and are summarised in table 1. –7– Preselection requirements Requirement tqH(bb̄) search tqH(τ τ ) search τlep τhad channel τhad τhad channel Trigger single-lepton trigger single-lepton trigger di-τ trigger Leptons =1 isolated e or µ =1 isolated e or µ no isolated e or µ — ≥1 τhad ≥2 τhad Electric charge (q) — q` × qτhad,1 < 0 qτhad,1 × qτhad,2 < 0 Jets ≥4 jets ≥3 jets ≥3 jets b-tagging ≥2 b-tagged jets =1 b-tagged jets =1 b-tagged jets Table 1. Summary of preselection requirements for the tqH(bb̄) and tqH(τ τ ) searches. The leading JHEP05(2019)123 and trailing τhad candidates are denoted by τhad,1 and τhad,2 respectively. 5 Signal and background modelling Signal and most background processes are modelled using Monte Carlo (MC) simulation. After the event preselection, the main background is tt̄ production, often in association with jets, denoted by tt̄+jets in the following. Small contributions arise from single-top- quark, W/Z+jets, multijet and diboson (W W, W Z, ZZ) production, as well as from the associated production of a vector boson V (V = W, Z) or a Higgs boson and a tt̄ pair (tt̄V and tt̄H). All backgrounds with prompt leptons, i.e. those originating from the decay of a W boson, a Z boson, or a τ -lepton, are estimated using samples of simulated events and initially normalised to their theoretical cross sections. In the simulation, the top-quark and SM Higgs boson masses are set to 172.5 GeV and 125 GeV, respectively, and the Higgs boson is allowed to decay into all SM particles with branching ratios calculated using Hdecay [61]. Backgrounds with non-prompt electrons or muons, with photons or jets misidentified as electrons, or with jets misidentified as τhad candidates, generically referred to as fake leptons, are estimated using data-driven methods. The background prediction is further improved during the statistical analysis by performing a likelihood fit to data using several signal-depleted analysis regions, as discussed in sections 6 and 7. 5.1 Simulated signal and background processes Samples of simulated tt̄ → W bHq events were generated with the next-to-leading-order (NLO) generator4 Madgraph5 aMC@NLO 2.4.3 [62] (referred to in the following as MG5 aMC) with the NNPDF3.0 NLO [63] parton distribution function (PDF) set and interfaced to Pythia 8.212 [64] with the NNPDF2.3 LO [65] PDF set for the modelling of parton showering, hadronisation, and the underlying event. The A14 [66] set of tuned parameters in Pythia controlling the description of multiparton interactions and initial- and final-state radiation, referred to as the tune, was used. The signal sample is normalised to the same total cross section as used for the inclusive tt̄ → W bW b sample (see discussion below) and assuming an arbitrary branching ratio of Bref (t → Hq) = 1%. The case of both 4 In the following, the order of a generator should be understood as referring to the order in the strong coupling constant at which the matrix-element calculation is performed. –8– top quarks decaying into Hq is neglected in the analysis given the existing upper limits on B(t → Hq) (section 1). The nominal sample used to model the tt̄ background was generated with the NLO generator Powheg-Box v2 [67–70] using the NNPDF3.0 NLO PDF set. The Powheg- Box model parameter hdamp , which controls matrix element to parton shower matching and effectively regulates the high-pT radiation, was set to 1.5 times the top-quark mass. The parton showers, hadronisation, and underlying event were modelled by Pythia 8.210 with the NNPDF2.3 LO PDF set in combination with the A14 tune. Alternative tt̄ simulation samples used to derive systematic uncertainties are described in section 8.3. The generated tt̄ samples are normalised to a theoretical cross section of 832+46 −51 pb, computed using JHEP05(2019)123 Top++ v2.0 [71] at next-to-next-to-leading order (NNLO), including resummation of next- to-next-to-leading logarithmic (NNLL) soft gluon terms [72–76]. The tt̄ background selected by the tqH(bb̄) search is enriched in tt̄+heavy-flavour pro- duction, and thus requires a more sophisticated treatment than provided by the nominal tt̄ sample; this treatment is briefly outlined below. A detailed discussion can be found in ref. [77]. The simulated tt̄ events are categorised depending on the flavour content of additional particle jets not originating from the decay of the tt̄ system. Events labelled as either tt̄+≥1b or tt̄+≥1c are generically referred to in the following as tt̄+HF events, where HF stands for heavy flavour. The remaining events are labelled as tt̄+light-jets events, including those with no additional jets. A finer categorisation of tt̄+≥1b events is considered for the purpose of applying further corrections and assigning systematic un- certainties associated with the modelling of heavy-flavour production in different event topologies [77]. In particular, the tt̄+≥1b events are reweighted to an NLO prediction in the four-flavour (4F) scheme of tt̄+≥1b production including parton showering [78], based on Sherpa+OpenLoops [79, 80] (referred to as SherpaOL in the following) using the CT10 4F PDF set. This reweighting is performed in such a way that the inter-normalisations of the tt̄+≥1b categories are at NLO accuracy, while preserving the tt̄+≥1b cross section of the nominal tt̄ sample. This reweighting is also applied to the alternative tt̄ samples that are used to study systematic uncertainties. Samples of single-top-quark events corresponding to the t-channel production mecha- nism were generated with the Powheg-Box v1 [81] generator, using the 4F scheme for the NLO matrix-element calculations and the fixed 4F CT10f4 [82] PDF set. Samples corre- sponding to the tW - and s-channel production mechanisms were generated with Powheg- Box v1 using the CT10 PDF set. Overlaps between the tt̄ and tW final states were avoided by using the diagram removal scheme [83]. The parton showers, hadronisation and the underlying event were modelled using Pythia 6.428 [84] with the CTEQ6L1 [85, 86] PDF set in combination with the Perugia 2012 tune [87]. The single-top-quark samples are normalised to the approximate NNLO theoretical cross sections [88–90]. Samples of W/Z+jets events were generated with the Sherpa 2.2.1 [79] generator. The matrix element was calculated for up to two partons at NLO and up to four partons at LO using Comix [91] and OpenLoops [80]. The matrix-element calculation is merged with the Sherpa parton shower [92] using the ME+PS@NLO prescription [93]. The PDF set used for the matrix-element calculation is NNPDF3.0 NNLO [63] with a dedicated –9– parton shower tuning developed for Sherpa. Separate samples were generated for differ- ent W/Z+jets categories using filters for a b-jet (W/Z+≥1b+jets), a c-jet and no b-jet (W/Z+≥1c+jets), and with a veto on b- and c-jets (W/Z+light-jets), which are combined into the inclusive W/Z+jets samples. Both the W +jets and Z+jets samples are normalised to their respective inclusive NNLO theoretical cross sections calculated with FEWZ [94]. Samples of W W/W Z/ZZ+jets events were generated with Sherpa 2.2.1 using the CT10 PDF set and include processes containing up to four electroweak vertices. In the case of W W/W Z+jets (ZZ+jets) the matrix element was calculated for zero (up to one) additional partons at NLO and up to three partons at LO using the same procedure as JHEP05(2019)123 for the W/Z+jets samples. The final states simulated require one of the bosons to decay leptonically and the other hadronically. All diboson samples are normalised to their NLO theoretical cross sections provided by Sherpa. Samples of tt̄V and tt̄H events were generated with MG5 aMC 2.2.1, using NLO matrix elements and the NNPDF3.0 NLO PDF set, and interfaced to Pythia 8.210 with the NNPDF2.3 LO PDF set and the A14 tune. Instead, the tt̄V samples used in the tqH(bb̄) search are based on LO matrix elements computed for up to two additional partons using the NNPDF3.0 NLO PDF set, and merged using the CKKW-L approach [95]. The tt̄V samples are normalised to the NLO cross section computed with MG5 aMC, while the tt̄H sample is normalised using the NLO cross section recommended in ref. [96]. All generated samples, except those produced with the Sherpa [79] event gener- ator, utilise EvtGen 1.2.0 [97] to model the decays of heavy-flavour hadrons. To model the effects of pile-up, events from minimum-bias interactions were generated using Pythia 8.186 [64] in combination with the A2 tune [98], and overlaid onto the simulated hard-scatter events according to the luminosity profile of the recorded data. The generated events were processed through a simulation [99] of the ATLAS detector geometry and re- sponse using Geant4 [100]. A faster simulation, where the full Geant4 simulation of the calorimeter response is replaced by a detailed parameterisation of the shower shapes [101], was adopted for some of the samples used to estimate systematic uncertainties in back- ground modelling. Simulated events were processed through the same reconstruction soft- ware as the data, and corrections were applied so that the object identification efficiencies, energy scales and energy resolutions match those determined from data control samples. 5.2 Backgrounds with fake leptons 5.2.1 Fake electrons and muons In the tqH(bb̄) search, the background from multijet production (multijet background in the following) contributes to the selected data sample via several production and misrecon- struction mechanisms. In the electron channel, it consists of non-prompt electrons (from semileptonic b- or c-hadron decays) as well as misidentified photons (from a conversion of a photon into an e+ e− pair) or jets with a high fraction of their energy deposited in the EM calorimeter. In the muon channel, the multijet background originates mainly from non- prompt muons. The multijet background normalisation and shape are estimated directly from data by using the matrix method technique [102, 103], which exploits differences in – 10 – lepton identification and isolation properties between prompt leptons and leptons that are either non-prompt or result from the misidentification of photons or jets. 5.2.2 Fake τ -lepton candidates In the tqH(τ τ ) search, the background with one or more fake τhad candidates mainly arises from tt̄ or multijet production, depending on the search channel, with W +jets production contributing to a lesser extent. Studies based on the simulation show that, for all the above processes, fake τhad candidates primarily result from the misidentification of light- quark jets, with the contribution from b-quarks and gluon jets playing a subdominant JHEP05(2019)123 role. It is also found that the fake rate decreases for all jet flavours as the τhad candidate pT increases. This background is estimated directly from data by defining control regions (CR) enriched in fake τhad candidates via loosened τhad requirements or flipped charge. These CRs do not overlap with the main search regions (SRs), discussed in section 7. The CR selection requirements are analogous to those used to define the different SRs, except that the leading (trailing) τhad candidate in the τlep τhad (τhad τhad ) channel is required to fail the medium τhad identification but pass the loose identification, or the two τhad candidates have the same charge. The fake τhad background prediction in a given SR is modelled by the distribution (referred to as the fake τhad template) derived from data in the corresponding CR. The fake τhad template is defined as the data distribution from which the contributions from the simulated backgrounds with real τhad candidates, originating primarily from W (→ τ ν)+jets and Z(→ τ τ )+jets, are subtracted. In the τlep τhad channel, simulation studies indicate that the fake τhad background composition is consistent between the SR and the CR, and dominated by tt̄ production. In the τhad τhad channel, the fake τhad background is expected to be dominated by multijet production. However, simulation studies indicate that the contribution of tt̄ events to the fake τhad background is higher in the SR than in the CR. Therefore, an appropriate number of simulated tt̄ events with fake τhad candidates in the CR is added to the fake τhad template to match the fake τhad background composition in the SR. In both the τlep τhad and τhad τhad channels, the fake τhad template in each SR is initially normalised to the estimated fake τhad background yield, defined as the data yield minus the contributions from the simulated backgrounds with real τhad candidates (assuming no signal contribution). During the statistical analysis, the normalisation of the fake τhad background in each SR is allowed to vary freely in the fit to data, as discussed in section 10.2. 6 Strategy for the tqH(bb̄) search This section presents an overview of the analysis strategy adopted in the tqH(bb̄) search, which closely follows that of the previous search performed on the Run 1 dataset [27]. 6.1 Event categorisation Given that the W → `ν and H → bb̄ decay modes are chosen, the tt̄ → W bHq signal is expected to have four jets in the final state, three of them originating from b-quarks, which – 11 – can be effectively exploited to suppress the background. Additional jets can also be present because of initial- or final-state radiation. However, the use of the 60% b-tagging efficiency operating point, characterised by a low mistag rate for c- and light-jets, results in both the tt̄ → W bHc and tt̄ → W bHu signals having a similar b-tag multiplicity distribution, with a very small fraction of events having four or more b-tagged jets. In order to optimise the sensitivity of the search, the selected events are categorised into different analysis regions depending on the number of jets (4, 5 and ≥6) and on the number of b-tagged jets (2, 3 and ≥4). Therefore, a total of nine analysis regions are considered: (4j, 2b), (4j, 3b), (4j, 4b), (5j, 2b), (5j, 3b), (5j, ≥4b), (≥6j, 2b), (≥6j, 3b), and (≥6j, ≥4b), where (nj, mb) indicates n selected jets and m b-tagged jets. JHEP05(2019)123 The overall rate and composition of the tt̄+jets background strongly depends on the jet and b-tag multiplicities, as illustrated in figure 1. Regions with exactly two b-tagged jets are dominated by tt̄+light-jets, while regions with at least four b-tagged jets are dominated by tt̄+≥1b. Intermediate compositions are found in regions with exactly three b-tagged jets. Most of the tt̄+light-jets background events in these regions have a b-tagged charm jet from the hadronic W boson decay, in addition to the two b-jets from the top-quark decays. In the regions with four or five jets and exactly three b-tagged jets, which dominate the sensitivity of this search, the selected signal events have a H → bb̄ decay in more than 97% of the events. The other regions have significantly lower signal-to-background ratios, but they are used to improve the tt̄+jets background prediction and constraining the related systematic uncertainties through a likelihood fit to data. Because of a somewhat larger fraction of tt̄ → W bHc signal in the regions with exactly three b-tagged jets, resulting from the higher mistag rate for c-jets than for light-jets, this analysis is expected to have slightly better sensitivity to a tt̄ → W bHc signal than to a tt̄ → W bHu signal. 6.2 Likelihood discriminant After event categorisation, the signal-to-background ratio is insufficient even in the best cases to achieve sensitivity, and a suitable discriminating variable between signal and back- ground needs to be constructed in order to improve the sensitivity of the search. Since both signal and background result from the tt̄ decay, their discrimination is a challenge and it is based on a few measured quantities. The most prominent features are the different resonances present in the decay (the Higgs boson in the case of the tt̄ → W bHq signal and a hadronically decaying W boson in the case of the tt̄ → W bW b background), and the different flavours of the jets forming those resonances. However, the large number of jets in the final state causes ambiguities in the calculation of these kinematic variables to discriminate signal events from background events. This search uses a likelihood (LH) discriminant similar to that developed in ref. [27]. The LH variable for a given event is defined as: P sig (x) L(x) = , P sig (x) + P bkg (x) where P sig (x) and P bkg (x) represent the probability density functions (pdf) of a given event under the signal hypothesis (tt̄ → W bHq) and under the background hypothesis – 12 – 107 Events ATLAS Data s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) tt→WbHu (ℬ =1%) 106 tqH(bb) search tt+light-jets Pre-Fit tt+≥1c tt+≥1b Non-tt 105 Total Bkg unc. 104 103 102 JHEP05(2019)123 1.5 10 Data / Bkg 1.25 1 0.75 0.5 5j, ≥4b ≥6j, ≥4b 4j, 2b 5j, 2b ≥6j, 2b 4j, 3b 5j, 3b ≥6j, 3b 4j, 4b Figure 1. tqH(bb̄) search: comparison between the data and predicted background for the event yields in each of the analysis regions considered before the fit to data (“Pre-Fit”). All events satisfy the preselection requirements, whereas those with exactly two b-tagged jets are in addition required to have a value of the likelihood discriminant above 0.6 (see section 6.2). Backgrounds are normalised to their nominal cross sections. The small contributions from W/Z+jets, single-top- quark, diboson and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. The expected tt̄ → W bHc and tt̄ → W bHu signals (dashed histograms) are shown separately normalised to B(t → Hq) = 1%. The bottom panel displays the ratio of data to the SM background (“Bkg”) prediction. The hashed area represents the total uncertainty of the background, excluding the normalisation uncertainty of the tt̄+ ≥ 1b background, which is determined via a likelihood fit to data. (tt̄ → W bW b), respectively. Both P sig and P bkg are functions of x, representing the four- momentum vectors of all final-state particles at the reconstruction level: the lepton, the missing transverse momentum, and the selected jets in a given analysis region. The value of the multivariate b-tagging discriminant for each jet is also included in x. As in ref. [27], P sig and P bkg are approximated as a product of one-dimensional pdfs over the set of two- body and three-body invariant masses that correspond to the expected resonances in the event (the leptonically decaying W boson, the Higgs boson or the hadronically decaying W boson, and the corresponding parent top quarks) and averaged over all possible parton- jet matching combinations. Combinations are weighted using the per-jet multivariate b- tagging discriminant value to suppress the impact from parton-jet assignments that are inconsistent with the correct flavour of the parton candidates. The invariant masses are computed from the reconstructed lepton, missing transverse momentum, and jets. After a suitable transformation of the three-body invariant masses (see ref. [27]), all considered invariant mass variables are largely uncorrelated, thus making possible the factorisation of P sig and P bkg as discussed above. Two background hypotheses are considered, corresponding to the dominant back- grounds in the analysis: tt̄+light-jets and tt̄+≥1b. Thus, P bkg is computed as the average – 13 – of the pdfs for the two hypotheses, weighted by their relative fractions found in simulated tt̄+jets events, which depend on the analysis region considered. Furthermore, in a signifi- cant fraction of tt̄ → W bHq simulated events (about 40–50% in regions with exactly three b-tagged jets), the light-quark jet from the hadronic top-quark decay is not among the selected jets. Similarly, in about 30–40% (50–90%) of simulated tt̄+light-jets (tt̄+ ≥ 1b) background events in regions with exactly three b-tagged jets, the light-quark jet originat- ing from the W boson decay is also not selected. Thus, the calculation of P sig and P bkg also includes an additional hypothesis to account for this topology, again weighted by the corresponding fractions. In this case, the invariant masses involving the missing jet are computed using the highest-pT jet not matched to a decay product from the tt̄ system. JHEP05(2019)123 Figure 2 shows a comparison between data and prediction in the most sensitive analysis region, (4j, 3b), for several kinematic variables associated with the reconstructed lepton, jets, and missing transverse momentum. The distributions shown correspond to the lepton pT , the ETmiss , the scalar sum of the transverse momenta of the jets, and the invariant mass distribution of the two b-tagged jets with lowest ∆R separation. The variables displayed do not correspond directly to those used internally in the evaluation the LH discriminant, as to build them it is necessary to select a particular signal or background hypothesis and a jet permutation. Instead, these distributions are shown to demonstrate that a good description of the data by the background prediction is observed in several kinematic variables related to the information used in the LH discriminant construction. Figure 3 compares the shape of the LH discriminant distribution between the tt̄ → W bHc and tt̄ → W bHu signals and the tt̄ → W bW b background in each of the analysis regions considered. Since this analysis has higher expected sensitivity to a tt̄ → W bHc signal than to a tt̄ → W bHu signal, in order to allow probing of the B(t → Hu) versus B(t → Hc) plane, the LH discriminant optimised for tt̄ → W bHc is used for both decay modes. It was verified that using the tt̄ → W bHc discriminant for the tt̄ → W bHu search does not result in a significant sensitivity loss. 7 Strategy for the tqH(τ τ ) search The analysis strategy adopted in the tqH(τ τ ) search closely follows that developed in ref. [104] and is summarised in this section. 7.1 Event categorisation and kinematic reconstruction In the tqH(τ τ ) search, the tt̄ → W bHq signal being probed is characterised by the presence of τ -leptons from the decay of the Higgs boson and at least four jets, only one of which originates from a b-quark. If one of the τ -leptons decays leptonically, an isolated electron or muon and significant ET miss is also expected. However, in a significant fraction of the events the lowest-pT jet from the W boson decay fails the minimum pT requirement of 30 GeV, resulting in signal events with only three jets reconstructed. In order to optimise the sensitivity of the search, the selected events are categorised into four SRs depending on the number of τlep and τhad candidates, and on the number of jets: (τlep τhad , 3j), (τlep τhad , ≥4j), (τhad τhad , 3j), and (τhad τhad , ≥4j). – 14 – Events / 20 GeV Events / 20 GeV ATLAS Data ATLAS Data 4500 6000 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) tt+light-jets tt+light-jets tqH(bb) search tt+≥1c 4000 tqH(bb) search tt+≥1c 5000 4j, 3b tt+≥1b 4j, 3b tt+≥1b Non-tt 3500 Non-tt Pre-Fit Pre-Fit Total Bkg unc. Total Bkg unc. 3000 4000 2500 3000 2000 2000 1500 1000 JHEP05(2019)123 1000 500 1.50 1.50 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 50 100 150 200 250 300 0 50 100 150 200 250 300 Lepton p [GeV] T Emiss T [GeV] (a) (b) Events / 50 GeV Events / 20 GeV ATLAS Data ATLAS Data 5000 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) tt+light-jets 3000 tt+light-jets tqH(bb) search tt+≥1c tqH(bb) search tt+≥1c 4000 4j, 3b tt+≥1b 4j, 3b tt+≥1b Non-tt 2500 Non-tt Pre-Fit Pre-Fit Total Bkg unc. Total Bkg unc. 3000 2000 1500 2000 1000 1000 500 1.50 1.50 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 100 150 200 250 300 350 400 450 500 550 600 0 50 100 150 200 250 300 Hhad T [GeV] mmin bb ∆R [GeV] (c) (d) Figure 2. tqH(bb̄) search: comparison between the data and predicted background after prese- lection for several kinematic distributions in the (4j, 3b) region before the fit to data (“Pre-Fit”). miss The distributions are shown for (a) lepton pT , (b) ET , (c) scalar sum of the transverse momenta had of the jets (HT ), and (d) the invariant mass of the two b-tagged jets with lowest ∆R separa- tion (mmin∆R bb ). The small contributions from tt̄V , tt̄H, single-top-quark, W/Z+jets, diboson, and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. The expected tt̄ → W bHc signal (solid red) corresponding to B(t → Hc) = 1% is also shown, added to the background prediction. The last bin in all figures contains the overflow. The bottom panel displays the ratio of data to the SM background (“Bkg”) prediction. The blue triangles indicate points that are outside the vertical range of the figure. The hashed area represents the total un- certainty of the background, excluding the normalisation uncertainty of the tt̄+ ≥ 1b background, which is determined via a likelihood fit to data. – 15 – Fraction of events / 0.1 Fraction of events / 0.1 Fraction of events / 0.1 0.22 ATLAS Simulation tt→WbWb 0.25 ATLAS Simulation tt→WbWb 1.2 ATLAS Simulation tt→WbWb s=13 TeV s=13 TeV s=13 TeV 0.2 tt→WbHc tt→WbHc tt→WbHc tqH(bb) search tqH(bb) search tqH(bb) search 0.18 tt→WbHu tt→WbHu tt→WbHu 4j, 2b 4j, 3b 1 4j, 4b 0.2 0.16 0.14 0.8 0.15 0.12 0.6 0.1 0.1 0.08 0.4 JHEP05(2019)123 0.06 0.04 0.05 0.2 0.02 0 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LH discriminant LH discriminant LH discriminant (a) (b) (c) 0.25 Fraction of events / 0.1 Fraction of events / 0.1 Fraction of events / 0.1 ATLAS Simulation tt→WbWb ATLAS Simulation tt→WbWb ATLAS Simulation tt→WbWb s=13 TeV 0.25 s=13 TeV s=13 TeV tt→WbHc tt→WbHc 1 tt→WbHc 0.2 tqH(bb) search tqH(bb) search tqH(bb) search tt→WbHu tt→WbHu tt→WbHu 5j, 2b 5j, 3b 5j, ≥4b 0.2 0.8 0.15 0.15 0.6 0.1 0.1 0.4 0.05 0.05 0.2 0 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LH discriminant LH discriminant LH discriminant (d) (e) (f) 0.35 Fraction of events / 0.1 Fraction of events / 0.1 Fraction of events / 0.1 0.25 ATLAS Simulation tt→WbWb ATLAS Simulation tt→WbWb ATLAS Simulation tt→WbWb s=13 TeV s=13 TeV 1 s=13 TeV tt→WbHc 0.3 tt→WbHc tt→WbHc tqH(bb) search tqH(bb) search tqH(bb) search tt→WbHu tt→WbHu tt→WbHu 0.2 ≥6j, 2b ≥6j, 3b ≥6j, ≥4b 0.25 0.8 0.15 0.2 0.6 0.15 0.1 0.4 0.1 0.05 0.2 0.05 0 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LH discriminant LH discriminant LH discriminant (g) (h) (i) Figure 3. tqH(bb̄) search: comparison of the distributions of the LH discriminant after preselection of the tt̄ → W bHc (red dashed) and tt̄ → W bHu (blue dotted) signals, and the tt̄ → W bW b background (black solid) in different regions considered in the analysis: (a) (4j, 2b), (b) (4j, 3b), (c) (4j, 4b), (d) (5j, 2b), (e) (5j, 3b), (f) (5j, ≥4b), (g) (≥6j, 2b), (h) (≥6j, 3b), and (i) (≥6j, ≥4b). In the regions with ≥4 b-tagged jets, the signal acceptance is small, which translates into a small number of events for the simulated samples. Therefore, only two bins are used for these distributions. – 16 – This event categorisation is primarily motivated by the different quality of the event kinematic reconstruction, depending on the amount of ET miss in the event (larger in τ τ lep had events compared with τhad τhad events), and whether a jet from the hadronic top-quark decay is missing or not (events with exactly three jets or at least four jets). The event kine- matic reconstruction is based on the strategy used in ref. [104], and is summarised below. Events with exactly three jets that are compatible with having a fully reconstructed hadronically decaying top quark (t → W b → qqb) are rejected, as the t → Hq decay cannot be reconstructed due to the missing light-quark jet. This compatibility is assessed via a likelihood function that depends on the reconstructed mass of the three-jet system and JHEP05(2019)123 the two non-b-tagged jets. For the remaining events, the selected jets are assigned to the different top-quark decay products via a criterion based on minimising a sum of angular distances between objects. Finally, the four-momenta of the invisible decay products for each τ -lepton decay are estimated by minimising a χ2 function based on the probability density functions for the angular distance of the visible and invisible products of the τ - lepton decay, and including Gaussian constraints on the τ -lepton mass, the Higgs boson mass and the measured ET miss within their expected resolutions. The resolution on the τ -lepton mass and the Higgs boson mass are taken to be 1.8 GeV and 20 GeV, respectively, miss is parameterised as a linear function of pP while the resolution on the measured ET ET , miss P with ET denoting the scalar sum of the pT of all physics objects contributing to the ET 2 reconstruction [60]. After the χ minimisation, the Higgs boson four-momentum, and hence its invariant mass, as well as the four-momentum of the parent top quark, are determined with better resolution. Following the event kinematic reconstruction, several kinematic variables that discriminate between signal and background are defined. These variables are used in the multivariate analysis discussed in the next section. 7.2 Multivariate discriminant Boosted decision trees are used in each SR to improve the separation between signal and background. In the training, only tt̄ → W (qq)bH(τ τ )q signal events are used against the total SM background (including both real and fake τhad contributions), whereas to obtain the result the contributions from tt̄ → W (`ν)bHq signal events are also taken into account. A large set of potential variables were investigated in each SR separately, and only those variables that led to better discrimination by the BDT were kept. The discrimination of a given variable was quantified by the “separation” and “importance” measures provided by the TMVA package [105]. The BDT input variables in each SR are listed in table 2 and defined in the following: • mfit τ τ : the invariant mass of the two τ -lepton candidates after the reconstruction of the neutrinos, indicating the reconstructed Higgs boson mass. • mHq : the invariant mass of the reconstructed Higgs boson and the associated light- quark jet in the t → Hq decay, corresponding to the reconstructed mass of the parent top quark. – 17 – τlep τhad τhad τhad Variable 3j ≥4j 3j ≥4j mfit ττ × × × × mHq × × × × mT,lep × × pT,1 × × × × pT,2 × × × × miss ET φ centrality × × × × miss ET,k × × × × miss ET,⊥ × × JHEP05(2019)123 mbj1 × × × × mlepj × × mτ j × × xfit 1 × × × × xfit 2 × × × × mbj1 j2 × × Table 2. tqH(τ τ ) search: discriminating variables used in the training of the BDT for each search region (denoted by ×). The description of each variable is provided in the text. • mT,lep : the transverse mass calculated from the lepton and p~Tmiss in the τlep τhad channel. • pT,1 and pT,2 : the transverse momenta of the lepton and τhad candidate (referred to as particles 1 and 2 respectively) in the τlep τhad channel, or the transverse momenta of the leading and trailing τhad candidates (referred to as particles 1 and 2 respectively) in the τhad τhad channel. • ETmiss φ centrality: a variable that quantifies the angular position of p ~Tmiss relative to the visible τ -lepton decay products in the transverse plane. It is defined as: miss sin(φmiss − φ1 ) + sin(φmiss − φ2 ) ET φ centrality = p 2 sin (φmiss − φ1 ) + sin2 (φmiss − φ2 ) where φmiss denotes the azimuthal angle of p~Tmiss , and φ1 and φ2 denote the azimuthal angles the two τ -lepton candidates (the lepton and τhad candidate in the τlep τhad channel, or the leading and trailing τhad candidates in the τhad τhad channel), referred to as particles 1 and 2 respectively. miss : the magnitude of the projection of the original p • ET,k ~Tmiss vector parallel to the fitted p~Tmiss vector, minus the magnitude of the fitted p~Tmiss vector. miss : the magnitude of the projection of the original p • ET,⊥ ~Tmiss vector perpendicular to miss the fitted p~T vector. • mbj1 : the invariant mass of the b-jet and the leading jet candidate from the hadroni- cally decaying W boson. – 18 – • mlepj : the invariant mass of the lepton and the jet that has the smallest angular distance to the τlep candidate. • mτ j : the invariant mass of the τhad candidate and the jet that has the smallest angular distance to the τhad candidate. • xfit fit 1 and x2 : the momentum fractions carried by the visible decay products from the two τ -lepton candidates (whether τlep or τhad ) per event. It is based on the best-fit four-momentum of the neutrino(s) according to the event reconstruction procedure outlined in section 7.1. JHEP05(2019)123 • mbj1 j2 : the invariant mass of the b-jet and the two jets originating from the W boson in the t → W b → j1 j2 b decay, corresponding to the reconstructed mass of the parent top quark. This variable is only defined for events with at least four jets. Among these variables, the most discriminating are mfit fit fit τ τ , pT,2 , x1 and x2 . A com- parison between data and the predicted background for some of these variables in each of the SRs considered is shown in figures 4 and 5. A good description of the data by the background model is observed in all cases. The level of discrimination between signal and background achieved by the BDTs is illustrated in figure 6. 8 Systematic uncertainties Several sources of systematic uncertainty that can affect the normalisation of signal and background and/or the shape of their corresponding discriminant distributions are consid- ered. Each source is considered to be uncorrelated with the other sources. Correlations of a given systematic uncertainty are maintained across processes and channels as appropriate. The following sections describe the systematic uncertainties considered. 8.1 Luminosity The uncertainty in the integrated luminosity is 2.1%, affecting the overall normalisation of all processes estimated from the simulation. It is derived, following a methodology similar to that detailed in ref. [106], and using the LUCID-2 detector for the baseline luminosity measurements [107], from a calibration of the luminosity scale using x–y beam- separation scans. 8.2 Reconstructed objects Uncertainties associated with electrons, muons, and τhad candidates arise from the trigger, reconstruction, identification and isolation (in the case of electrons and muons) efficiencies, as well as the momentum scale and resolution. These are measured using Z → `+ `− and J/ψ → `+ `− events (` = e, µ) [41, 43] in the case of electrons and muons, and using Z → τ + τ − events in the case of τhad candidates [59]. Uncertainties associated with jets arise from the jet energy scale and resolution, and the efficiency to pass the JVT requirements. The largest contribution results from the – 19 – 1400 Events / 5 GeV Events / 5 GeV Data 1200 Data ATLAS ATLAS s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) 1200 Fake τhad Fake τhad tqH(ττ) search Top (real τhad) 1000 tqH(ττ) search Top (real τhad) τlepτhad, 3j Z→ττ τlepτhad, ≥4j Z→ττ 1000 Other Other Pre-Fit Pre-Fit Total Bkg unc. 800 Total Bkg unc. 800 600 600 400 400 JHEP05(2019)123 200 200 1.50 1.50 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 40 60 80 100 120 140 160 40 60 80 100 120 140 160 mτfitτ [GeV] mτfitτ [GeV] (a) (b) Events / 5 GeV Events / 5 GeV 3000 ATLAS Data ATLAS Data s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) Fake τhad 2500 Fake τhad 2500 tqH(ττ) search Top (real τhad) tqH(ττ) search Top (real τhad) τlepτhad, 3j Z→ττ τlepτhad, ≥4j Z→ττ Pre-Fit Other 2000 Pre-Fit Other 2000 Total Bkg unc. Total Bkg unc. 1500 1500 1000 1000 500 500 1.50 1.50 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 30 40 50 60 70 80 90 100 30 40 50 60 70 80 90 100 p [GeV] p [GeV] T,2 T,2 (c) (d) Figure 4. tqH(τ τ ) search: comparison between the data and predicted background after prese- lection for the distributions of two of the most discriminating BDT input variables in the τlep τhad channel before the fit to data (“Pre-Fit”). The distributions are shown for mfit τ τ in (a) the (τlep τhad , 3j) region and (b) the (τlep τhad , ≥4j) region, and for pT,2 in (c) the (τlep τhad , 3j) region and (d) the (τlep τhad , ≥4j) region. The contributions with real τhad candidates from tt̄, tt̄V , tt̄H, and single-top- quark backgrounds are combined into a single background source referred to as “Top (real τhad )”, whereas the small contributions from Z → `+ `− (` = e, µ) and diboson backgrounds are combined into “Other”. The expected tt̄ → W bHc signal (solid red) corresponding to B(t → Hc) = 1% is also shown, added to the background prediction. The first and the last bins in all figures contain the underflow and overflow respectively. The bottom panel displays the ratio of data to the SM back- ground (“Bkg”) prediction. The hashed area represents the total uncertainty of the background, excluding the normalisation uncertainty of the fake τhad background, which is determined via a likelihood fit to data. – 20 – 450 Events / 10 GeV Events / 10 GeV ATLAS Data ATLAS Data 400 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) 400 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) Fake τhad Fake τhad 350 tqH(ττ) search Top (real τhad) tqH(ττ) search Top (real τhad) 350 τhadτhad, 3j Z→ττ τhadτhad, ≥4j Z→ττ 300 Pre-Fit Other Other Total Bkg unc. 300 Pre-Fit Total Bkg unc. 250 250 200 200 150 150 100 100 JHEP05(2019)123 50 50 1.50 1.50 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 40 60 80 100 120 140 160 40 60 80 100 120 140 160 mτfitτ [GeV] mτfitτ [GeV] (a) (b) Events / 0.1 Events / 0.1 ATLAS Data 400 ATLAS Data 450 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) Fake τhad Fake τhad 400 tqH(ττ) search Top (real τhad) 350 tqH(ττ) search Top (real τhad) τhadτhad, 3j Z→ττ τhadτhad, ≥4j Z→ττ 350 Other 300 Pre-Fit Other Pre-Fit Total Bkg unc. Total Bkg unc. 300 250 250 200 200 150 150 100 100 50 50 1.50 1.50 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 xfit 1 xfit 1 (c) (d) Figure 5. tqH(τ τ ) search: comparison between the data and predicted background after prese- lection for the distributions of two of the most discriminating BDT input variables in the τhad τhad channel before the fit to data (“Pre-Fit”). The distributions are shown for mfit τ τ in (a) the (τhad τhad , fit 3j) region and (b) the (τhad τhad , ≥4j) region, and for x1 in (c) the (τhad τhad , 3j) region and (d) the (τhad τhad , ≥4j) region. The contributions with real τhad candidates from tt̄, tt̄V , tt̄H, and single-top- quark backgrounds are combined into a single background source referred to as “Top (real τhad )”, whereas the small contributions from Z → `+ `− (` = e, µ) and diboson backgrounds are combined into “Other”. The expected tt̄ → W bHc signal (solid red) corresponding to B(t → Hc) = 1% is also shown, added to the background prediction. The first and the last bins in the figures in (a) and (b) contain the underflow and overflow respectively. The bottom panel displays the ratio of data to the SM background (“Bkg”) prediction. The hashed area represents the total uncertainty of the background, excluding the normalisation uncertainty of the fake τhad background, which is determined via a likelihood fit to data. – 21 – 0.7 0.7 Fraction of events / 0.2 Fraction of events / 0.2 ATLAS Simulation Total background ATLAS Simulation Total background s=13 TeV tt→WbHc s=13 TeV tt→WbHc 0.6 tqH(ττ) search 0.6 tqH(ττ) search τlepτhad, 3j tt→WbHu τlepτhad, ≥4j tt→WbHu 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 JHEP05(2019)123 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 BDT discriminant BDT discriminant (a) (b) Fraction of events / 0.2 Fraction of events / 0.2 0.6 ATLAS Simulation Total background ATLAS Simulation Total background 0.5 s=13 TeV tt→WbHc s=13 TeV tt→WbHc tqH(ττ) search tqH(ττ) search τhadτhad, 3j tt→WbHu τhadτhad, ≥4j tt→WbHu 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 BDT discriminant BDT discriminant (c) (d) Figure 6. tqH(τ τ ) search: comparison of the distributions of the BDT discriminant after pre- selection of the tt̄ → W bHc (red dashed) and tt̄ → W bHu (blue dotted) signals, and the total background (black solid) in the different search regions considered: (a) (τlep τhad , 3j), (b) (τlep τhad , ≥4j), (c) (τhad τhad , 3j), and (d) (τhad τhad , ≥4j). jet energy scale, whose uncertainty dependence on jet pT and η, jet flavour, and pile-up treatment, is split into 21 uncorrelated components that are treated independently [48]. Uncertainties associated with energy scales and resolutions of leptons and jets are propagated to ET miss . Additional uncertainties originating from the modelling of the under- lying event, in particular its impact on the pT scale and resolution of unclustered energy, are negligible. Efficiencies to tag b-jets and c-jets in the simulation are corrected to match the effi- ciencies in data by pT -dependent factors, whereas the light-jet efficiency is scaled by pT - and η-dependent factors. The b-jet efficiency is measured in a data sample enriched in tt̄ events [108], while the c-jet efficiency is measured using tt̄ events [109] or W +c-jet events [53]. The light-jet efficiency is measured in a multijet data sample enriched in light- flavour jets [110]. Since the tt̄ sample used to measure the c-jet tagging efficiency overlaps with the analysis sample, the tqH(bb̄) search uses instead the W +c-jet scale factors. In the case of the tqH(bb̄) (tqH(τ τ )) search, the uncertainties in these scale factors include a to- – 22 – tal of 6 independent sources affecting b-jets, 1 (2) source(s) affecting c-jets, and 17 sources affecting light-jets. These systematic uncertainties are taken as uncorrelated between b- jets, c-jets, and light-jets. An additional uncertainty is included due to the extrapolation of these corrections to jets with pT beyond the kinematic reach of the data calibration samples used (pT > 300 GeV for b- and c-jets, and pT > 750 GeV for light-jets); it is taken to be correlated among the three jet flavours. Since the fraction of signal and background in this kinematic regime is very small, these uncertainties have a negligible impact in the analyses. Finally, an uncertainty related to the application of c-jet scale factors to τ -jets is considered, which also has a negligible impact. JHEP05(2019)123 8.3 Background modelling A number of sources of systematic uncertainty affecting the modelling of tt̄+jets are con- sidered. An uncertainty of 6% is assigned to the inclusive tt̄ production cross section [71], including contributions from varying the factorisation and renormalisation scales, as well as from the top-quark mass, the PDF and αS . The latter two represent the largest contribu- tion to the overall theoretical uncertainty in the cross section and were calculated using the PDF4LHC prescription [111] with the MSTW 2008 68% CL NNLO, CT10 NNLO [82, 112] and NNPDF2.3 5F FFN [65] PDF sets. The uncertainty associated with the choice of NLO generator is derived by comparing the nominal prediction from Powheg-Box+Pythia 8 with a prediction from Sherpa 2.2.1. For the latter, the matrix-element calculation is performed for up to two partons at NLO and up to four partons at LO using Comix and OpenLoops, and merged with the Sherpa parton shower using the ME+PS@NLO prescription. The uncertainty due to the choice of parton shower and hadronisation (PS & Had) model is derived by comparing the predictions from Powheg-Box interfaced ei- ther to Pythia 8 or Herwig 7. The latter uses the MMHT2014 LO [113] PDF set in combination with the H7UE tune [114]. The uncertainty in the modelling of additional radiation is assessed with two alternative Powheg-Box+Pythia 8 samples: a sample with increased radiation (referred to as radHi) is obtained by decreasing the renormalisa- tion and factorisation scales by a factor of two, doubling the hdamp parameter, and using the Var3c upward variation of the A14 parameter set; a sample with decreased radiation (referred to as radLow) is obtained by increasing the scales by a factor of two and using the Var3c downward variation of the A14 set [115]. In the case of the tqH(bb̄) search, where the tt̄+HF background plays a prominent role (see figure 1), a more detailed treatment of its associated systematic uncertainties is used. In particular, since several analysis regions have a sufficiently large number of tt̄+≥1b background events, its normalisation is determined in the fit to data. In the case of the tt̄+≥1c normalisation, an uncertainty of 50% is assumed, as the fit to the data is unable to precisely determine it, and the analysis has very limited sensitivity to this uncertainty. Since the diagrams that contribute to tt̄+light-jets, tt̄+≥1c, and tt̄+≥1b production are different, all above uncertainties in tt̄+jets background modelling (NLO generator, PS & Had, and radHi/radLow), except the uncertainty of the inclusive cross section, are con- sidered to be uncorrelated among these processes. Additional uncertainties of the tt̄+≥1b background are considered associated with the NLO prediction from SherpaOL, which – 23 – is used for reweighting the nominal Powheg-Box+Pythia 8 prediction. These include three different scale variations, a different shower-recoil model scheme, and two alterna- tive PDF sets (MSTW 2008 NLO and NNPDF2.3 NLO). Additional uncertainties are assessed for the contributions to the tt̄+≥1b background originating from multiple parton interactions. Finally, an additional uncertainty is assigned to the tt̄+≥1b background by comparing the predictions from Powheg-Box+Pythia 8 and SherpaOL 4F (5F vs 4F). In the derivation of the above uncertainties, the overall normalisations of the tt̄+≥1c and tt̄+≥1b backgrounds at the particle level are fixed to the nominal prediction. In order to maintain the inclusive tt̄ cross section, the normalisation of the tt̄+light-jets background at the particle level is adjusted accordingly. JHEP05(2019)123 Uncertainties affecting the normalisation of the V +jets background are estimated for the sum of W +jets and Z+jets, and separately for V +light-jets, V +≥1c+jets, and V +≥1b+jets subprocesses. The total normalisation uncertainty of V +jets processes is es- timated by comparing the data and total background prediction in the different analysis regions considered, but requiring exactly zero b-tagged jets. Agreement between data and predicted background in these modified regions, which are dominated by V +light-jets, is found to be within approximately 30%. This bound is taken to be the normalisation un- certainty, correlated across all V +jets subprocesses. Since Sherpa 2.2 has been found to underestimate V +heavy-flavour production by about a factor of 1.3 [116], additional 30% normalisation uncertainties are assumed for V +≥1c+jets and V +≥1b+jets subprocesses, considered uncorrelated between them. Uncertainties affecting the modelling of the single-top-quark background include a +5%/−4% uncertainty of the total cross section estimated as a weighted average of the theoretical uncertainties in t-, tW - and s-channel production [88–90]. Additional uncer- tainties associated with the modelling of additional radiation are assessed by comparing the nominal samples with alternative samples where generator parameters are varied. For the t- and tW -channel processes, an uncertainty due to the choice of parton shower and hadro- nisation model is derived by comparing events produced by Powheg-Box interfaced to Pythia 6 or Herwig++. These uncertainties are treated as fully correlated among single- top-quark production processes, but uncorrelated with the corresponding uncertainty of the tt̄+jets background. An additional systematic uncertainty in tW -channel production concerning the separation between tt̄ and tW at NLO is assessed by comparing the nominal sample, which uses the diagram removal scheme [117], with an alternative sample using the diagram subtraction scheme [117]. Uncertainties of the diboson background normalisation include 5% from the NLO the- ory cross sections [118, 119], as well as an additional 24% normalisation uncertainty added in quadrature for each additional inclusive jet-multiplicity bin, based on a comparison among different algorithms for merging LO matrix elements and parton showers [120] (it is assumed that two jets originate from the W/Z decay, as in W W/W Z → `νjj). There- √ fore, the total normalisation uncertainty is 5% ⊕ N − 2 × 24%, where N is the selected jet multiplicity, resulting in 34%, 42%, and 48%, for events with exactly 4 jets, exactly 5 jets, and ≥6 jets, respectively. Recent comparisons between data and Sherpa 2.1.1 for W Z(→ `ν``)+ ≥4 jets show agreement within the experimental uncertainty of approx- – 24 – imately 40% [121], which further justifies the above uncertainty. Given the very small contribution of this background to the total prediction, the final result is not affected by the assumed modelling uncertainties. Uncertainties of the tt̄V and tt̄H cross sections are 15% and +10%/−13%, respectively, from the uncertainties of their respective NLO theoretical cross sections [96, 122, 123]. Uncertainties of the data-driven multijet background in the tqH(bb̄) search include contributions from the limited size of the data sample, particularly at high jet and b- tag multiplicities, as well as from the uncertainty in the rate of fake leptons, estimated in different control regions (e.g. selected with an upper requirement on either ET miss or mWT ). A combined normalisation uncertainty of 50% due to all these effects is assigned, JHEP05(2019)123 which is taken as correlated across jet and b-tag multiplicity bins, but uncorrelated be- tween electron and muon channels. No explicit shape uncertainty is assigned since the large statistical uncertainties associated with the multijet background prediction, which are uncorrelated between bins in the final discriminant distribution, effectively cover all possible shape uncertainties. Uncertainties of the data-driven fake τhad background in the tqH(τ τ ) search are ob- tained by using additional signal-depleted regions. The construction is similar to that of the SRs and corresponding CRs discussed in section 5.2, but employing further loosened τhad identification criteria, and thus referred to as “loose SR” and “loose CR”. In each loose SR, after subtracting the small simulation-predicted contribution from real τhad can- didates, the relative difference in the shape of the distribution between the remaining data and the fake τhad background estimate based on its associated loose CR is assigned as an uncertainty of the prediction in the nominal SR. In addition, a 30% uncertainty is applied to the fraction of tt̄ events with a fake τhad candidate from the simulation that are added to the fake τhad template in the τhad τhad channel as part of the fake τhad background esti- mation procedure. This uncertainty, associated with the modelling of the fake τhad rate by the simulation, is estimated by comparing data and simulation in a sample enriched in tt̄ dilepton events plus a fake τhad candidate. The same uncertainty is assigned to the selected signal events with fake τhad candidates. In addition, a systematic uncertainty is assigned to account for the different fractional composition of particles (various types of leptons and partons) producing the fake τhad candidates between each SR and its corresponding CR in the tt̄ simulation. Finally, the normalisation of the fake τhad background in each SR is determined in the fit to data. 8.4 Signal modelling Several normalisation and shape uncertainties are taken into account for the tt̄ → W bHq signal. The uncertainty of the tt̄ cross section also applies to the tt̄ → W bHq signal and is taken to be the same as, and fully correlated with, the uncertainty assigned to the tt̄ → W bW b background. Uncertainties of the Higgs boson branching ratios are taken into account following the recommendation in ref. [96]. Additional uncertainties associated with the modelling of additional radiation, with the choice of NLO generator, and with the choice of parton shower and hadronisation model, are estimated from the comparison of the nominal and alternative tt̄ → W bW b background samples (discussed in section 8.3) and – 25 – applied to tt̄ → W bHq signal. These modelling uncertainties are taken to be uncorrelated with those affecting the tt̄ → W bW b background. 9 Statistical analysis For each search, the final discriminant distributions across all analysis regions considered are jointly analysed to test for the presence of a signal. The statistical analysis uses a binned likelihood function L(µ, θ) constructed as a product of Poisson probability terms over all bins considered in the search. This function depends on the signal-strength parameter µ, defined as a factor multiplying the expected yield of tt̄ → W bHq signal events normalised JHEP05(2019)123 to a reference branching ratio Bref (t → Hq) = 1%, and θ, a set of nuisance parameters that encode the effect of systematic uncertainties on the signal and background expectations. Therefore, the expected total number of events in a given bin depends on µ and θ. All nuisance parameters are subject to Gaussian or log-normal constraints in the likelihood, with the exception of a few parameters that control the normalisation of some background components (e.g. the tt̄+≥1b background in the case of the tqH(bb̄) search), which are treated as free parameters in the fit. For a given value of µ, the nuisance parameters θ allow variations of the expectations for signal and background according to the corresponding systematic uncertainties, and their fitted values result in the deviations from the nominal expectations that globally provide the best fit to the data. This procedure allows a reduction of the impact of sys- tematic uncertainties on the search sensitivity by taking advantage of the highly populated background-dominated bins included in the likelihood fit. Statistical uncertainties in each bin of the predicted final discriminant distributions are taken into account by dedicated pa- rameters in the fit. The best-fit B(t → Hq) is obtained by performing a binned likelihood fit to the data under the signal-plus-background hypothesis, maximising the likelihood function L(µ, θ) over µ and θ. The fitting procedure was initially validated through extensive studies using mock data, defined as the sum of all predicted backgrounds plus an injected signal of variable strength, as well as by performing fits to real data where bins of the final discriminant variable with a signal contamination above 5% are excluded (referred to as blinding requirements). In both cases, the robustness of the model for systematic uncertainties is established by verifying the stability of the fitted background when varying assumptions about some of the leading sources of uncertainty. After this, the blinding requirements are removed in the data and a fit under the signal-plus-background hypothesis is performed. Further checks involve the comparison of the fitted nuisance parameters before and after removal of the blinding requirements, and their values are found to be consistent. In addition, it is verified that the fit is able to correctly determine the strength of a simulated signal injected into the real data. The test statistic qµ is defined as the profile likelihood ratio, qµ = −2 ln(L(µ, θ̂µ )/L(µ̂, θ̂)), where µ̂ and θ̂ are the values of the parameters that maximise the likelihood function (subject to the constraint 0 ≤ µ̂ ≤ µ), and θ̂µ are the values of the nuisance parameters that maximise the likelihood function for a given value of µ. The test – 26 – statistic qµ is evaluated with the RooFit package [124, 125]. A related statistic is used to determine whether the observed data is compatible with the background-only hypothesis (the so-called discovery test) by setting µ = 0 in the profile likelihood ratio and leaving µ̂ unconstrained: q0 = −2 ln(L(0, θ̂0 )/L(µ̂, θ̂)). The p-value (referred to as p0 ), representing the level of agreement between the data and the background-only hypothesis, is estimated by integrating the distribution of q0 based on the asymptotic formulae in ref. [126], above the observed value of q0 in the data. Upper limits on µ, and thus on B(t → Hq), are derived by using qµ in the CLs method [127, 128]. For a given signal scenario, values of the B(t → Hq) yielding CLs < 0.05, where CLs is computed using the asymptotic approximation [126], are excluded at ≥ 95% CL. JHEP05(2019)123 10 Results This section presents the results obtained from the individual searches for tt̄ → W bHq, as well as their combination, following the statistical analysis discussed in section 9. 10.1 tqH(bb̄) search A binned likelihood fit under the signal-plus-background hypothesis is performed on the LH discriminant distributions in the nine analysis regions considered. In the regions with exactly three b-tagged jets, which have the highest sensitivity, the full LH distribution is used with ten equal-width bins. In contrast, in the regions with at least four b-tagged jets, which have a limited number of data events and a small signal fraction, only two equal-width bins are used. Finally, in the regions with exactly two b-tagged jets the total event yield after requiring the LH discriminant to be above 0.6, is used. The unconstrained parameters of the fit are the signal strength and a global normalisation factor applied to the tt̄+≥1b background common to all analysis regions. Figures 7 and 8 show a comparison of the LH discriminant for data and prediction in the regions with exactly three and at least four b-tagged jets, both before and after performing the fit to data, in the case of the tt̄ → W bHc search. Tables summarising the pre-fit and post-fit yields can be found in appendix A. The best-fit branching ratio obtained is B(t → Hc) = [−0.2+0.7 +2.2 −0.7 (stat)−2.3 (syst)] × 10−3 , assuming B(t → Hu) = 0. A similar fit is performed for the tt̄ → W bHu search, yielding B(t → Hu) = [0.2+0.8 −0.7 (stat)−2.9 (syst)] × 10 , assuming B(t → Hc) = 0. The +2.5 −3 total uncertainties of the measured branching ratios are dominated by systematic uncer- tainties. The large number of events in the analysis regions considered, together with their different background compositions, allows the fit to place constraints on the combined effect of several sources of systematic uncertainty. As a result, an improved background prediction is obtained with a significantly reduced uncertainty, not only in the signal- depleted regions, but also in the most sensitive analysis regions for this search, (4j, 3b) and (5j, 3b). The regions with two b-tagged jets are used to constrain the leading uncertainties affecting the tt̄+light-jets background prediction, while the channels with at least four b- tagged jets are sensitive to the uncertainties affecting the tt̄+HF background prediction. – 27 – In particular, one of the main corrections applied by the fit is an increase of the tt̄+ ≥ 1b normalisation by a factor of 1.17 ± 0.15 relative to the nominal prediction by adjusting the corresponding nuisance parameter. The tt̄+ ≥ 1c normalisation is also increased, by a factor of 1.34 ± 0.40. These corrections are in agreement with those found in ref. [77]. Additionally, a few nuisance parameters are adjusted by the fit, with the largest effects corresponding to the leading nuisance parameters related to the b-tagging and c-tagging calibrations (by about 0.8 standard deviations), and those related to tt̄+ ≥ 1b and tt̄+ ≥ 1c modelling, which are based on a comparison with alternative generators (by 0.5 standard deviations or less). The leading uncertainties affecting the signal extraction by the fit are related to the c-tagging calibration (∆B ∼ 1.5 × 10−3 ), followed by the tt̄+light-jets PS JHEP05(2019)123 & Had uncertainty (∆B ∼ 1.2 × 10−3 ). Smaller contributions (∆B ∼ 0.5–1.0 × 10−3 each) result from the uncertainties associated with the tt̄+ ≥ 1b 5F vs 4F comparison, the dependence of jet energy scale on the jet flavour, the uncertainty of the tt̄+ ≥ 1c normalisation, and the limited size of the simulated samples in some of the bins with the highest signal-to-background ratio. The uncertainty most strongly constrained by the fit is that related to the c-tagging calibration. It is reduced by about a factor of two of its value as originally determined in W +c-jet events [53]. This is possible because the fit exploits the large number of tt̄ events with two and three b-tagged jets to effectively perform a c-tagging calibration, whose results are found to be consistent with those of ref. [109]. Beyond the constraints on a few individual uncertainties, the significant reduction of the total background uncertainty achieved by the fit primarily derives from the anti-correlations found among systematic uncertainties from different sources. In the absence of a significant excess of data events above the background expectation, 95% CL limits are set on B(t → Hc) and B(t → Hu). The observed (expected) 95% CL upper limits on the branching ratios are B(t → Hc) < 4.2 × 10−3 (4.0 × 10−3 ) and B(t → Hu) < 5.2 × 10−3 (4.9 × 10−3 ). 10.2 tqH(τ τ ) search A binned likelihood fit under the signal-plus-background hypothesis is performed on the BDT discriminant distributions in the four analysis regions considered. The unconstrained parameters of the fit are the signal strength, and four independent parameters associated with the normalisation of the fake τhad background in each of the analysis regions. No significant pulls or constraints are obtained for the fitted nuisance parameters, resulting in a post-fit background prediction in each analysis region that is very close to the pre-fit prediction, albeit with reduced uncertainties due to the anti-correlations among sources of systematic uncertainty resulting from the fit. Figure 9 shows a comparison of the data and prediction for the BDT discriminant distribution in the (τlep τhad , 3j) and (τlep τhad , ≥4j) regions, both pre- and post-fit to data, in the case of the tt̄ → W bHc search. A similar comparison for the (τhad τhad , 3j) and (τhad τhad , ≥4j) regions is shown in figure 10. Tables summarising the pre-fit and post-fit yields can be found in appendix B. The best-fit branching ratio obtained is B(t → Hc) = [−4.4+7.7 +6.2 −7.0 (stat)−4.9 (syst)] × 10−4 , assuming B(t → Hu) = 0. The best-fit normalisation factors for the fake τhad background are: 0.82 ± 0.23 in the (τlep τhad , 3j) region, 0.84+0.25 −0.28 in the (τlep τhad , ≥4j) – 28 – Events / 0.1 Events / 0.1 Events / 0.1 3000 ATLAS Data ATLAS Data ATLAS Data 3500 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) 5000 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) tt+light-jets tt+light-jets tt+light-jets 2500 tqH(bb) search tt+≥1c 3000 tqH(bb) search tt+≥1c tqH(bb) search tt+≥1c 4j, 3b tt+≥1b 5j, 3b tt+≥1b ≥6j, 3b tt+≥1b Non-tt Non-tt 4000 Non-tt Pre-Fit Pre-Fit Pre-Fit Total Bkg unc. 2500 Total Bkg unc. Total Bkg unc. 2000 2000 3000 1500 1500 2000 1000 JHEP05(2019)123 1000 500 1000 500 1.50 1.50 1.50 Data / Bkg Data / Bkg Data / Bkg 1.25 1.25 1.25 1 1 1 0.75 0.75 0.75 0.5 0.5 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LH discriminant LH discriminant LH discriminant (a) (b) (c) Events / 0.1 Events / 0.1 Events / 0.1 3000 ATLAS Data ATLAS Data ATLAS Data 3500 s = 13 TeV, 36.1 fb-1 tt→WbHc s = 13 TeV, 36.1 fb-1 tt→WbHc 5000 s = 13 TeV, 36.1 fb-1 tt→WbHc tt+light-jets tt+light-jets tt+light-jets 2500 tqH(bb) search tt+≥1c 3000 tqH(bb) search tt+≥1c tqH(bb) search tt+≥1c 4j, 3b tt+≥1b 5j, 3b tt+≥1b ≥6j, 3b tt+≥1b Post-Fit Non-tt Post-Fit Non-tt 4000 Post-Fit Non-tt 2000 Total Pred unc. 2500 Total Pred unc. Total Pred unc. 2000 3000 1500 1500 2000 1000 1000 500 1000 500 1.50 1.50 1.50 Data / Pred Data / Pred Data / Pred 1.25 1.25 1.25 1 1 1 0.75 0.75 0.75 0.5 0.5 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LH discriminant LH discriminant LH discriminant (d) (e) (f) Figure 7. tqH(bb̄) search: comparison between the data and prediction for the LH discriminant distribution in the regions with three b-tagged jets, before and after the fit to data (“Pre-Fit” and “Post-Fit”, respectively) under the signal-plus-background hypothesis. Shown are the (4j, 3b) region (a) pre-fit and (d) post-fit, the (5j, 3b) region (b) pre-fit and (e) post-fit, and the (≥6j, 3b) region (c) pre-fit and (f) post-fit. The small contributions from tt̄V , tt̄H, single-top-quark, W/Z+jets, diboson, and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. In the pre-fit figures the expected tt̄ → W bHc signal (solid red) corresponding to B(t → Hc) = 1% is also shown, added to the background prediction. In the post-fit figures, the tt̄ → W bHc signal is normalised using the best-fit branching ratio, B(t → Hc) = (−0.2+2.3 −2.4 )×10 −3 . The bottom panels display the ratios of data to either the SM background prediction before the fit (“Bkg”) or the total signal-plus-background prediction after the fit (“Pred”). The hashed area represents the total uncertainty of the background. In the case of the pre-fit background uncertainty, the normalisation uncertainty of the tt̄+ ≥ 1b background is not included. – 29 – Events / 0.5 Events / 0.5 Events / 0.5 1800 200 ATLAS Data ATLAS Data ATLAS Data 600 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) 1600 s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) 180 tt+light-jets tt+light-jets tt+light-jets tqH(bb) search tt+≥1c tqH(bb) search tt+≥1c tqH(bb) search tt+≥1c 500 1400 ≥6j, ≥4b 160 4j, 4b tt+≥1b 5j, ≥4b tt+≥1b tt+≥1b Pre-Fit Non-tt Pre-Fit Non-tt Pre-Fit Non-tt 140 Total Bkg unc. Total Bkg unc. 1200 Total Bkg unc. 400 120 1000 100 300 800 80 200 600 JHEP05(2019)123 60 400 40 100 20 200 1.50 1.50 1.50 Data / Bkg Data / Bkg Data / Bkg 1.25 1.25 1.25 1 1 1 0.75 0.75 0.75 0.5 0.5 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LH discriminant LH discriminant LH discriminant (a) (b) (c) Events / 0.5 Events / 0.5 Events / 0.5 1800 200 ATLAS Data ATLAS Data ATLAS Data 600 s = 13 TeV, 36.1 fb-1 tt→WbHc s = 13 TeV, 36.1 fb-1 tt→WbHc 1600 s = 13 TeV, 36.1 fb-1 tt→WbHc 180 tt+light-jets tt+light-jets tt+light-jets tqH(bb) search tt+≥1c tqH(bb) search tt+≥1c tqH(bb) search tt+≥1c 500 1400 ≥6j, ≥4b 160 4j, 4b tt+≥1b 5j, ≥4b tt+≥1b tt+≥1b Post-Fit Non-tt Post-Fit Non-tt Post-Fit Non-tt 140 Total Pred unc. Total Pred unc. 1200 Total Pred unc. 400 120 1000 100 300 800 80 200 600 60 400 40 100 20 200 1.50 1.50 1.50 Data / Pred Data / Pred Data / Pred 1.25 1.25 1.25 1 1 1 0.75 0.75 0.75 0.5 0.5 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 LH discriminant LH discriminant LH discriminant (d) (e) (f) Figure 8. tqH(bb̄) search: comparison between the data and prediction for the LH discriminant distribution in the regions with at least four b-tagged jets, before and after the fit to data (“Pre-Fit” and “Post-Fit”, respectively) under the signal-plus-background hypothesis. Shown are the (4j, 4b) region (a) pre-fit and (d) post-fit, the (5j, ≥4b) region (b) pre-fit and (e) post-fit, and the (≥6j, ≥4b) region (c) pre-fit and (f) post-fit. The small contributions from tt̄V , tt̄H, single-top-quark, W/Z+jets, diboson, and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. In the pre-fit figures the expected tt̄ → W bHc signal (solid red) corresponding to B(t → Hc) = 1% is also shown, added to the background prediction. In the post-fit figures, the tt̄ → W bHc signal is normalised using the best-fit branching ratio, B(t → Hc) = (−0.2+2.3 −2.4 )×10 −3 . The bottom panels display the ratios of data to either the SM background prediction before the fit (“Bkg”) or the total signal-plus-background prediction after the fit (“Pred”). The hashed area represents the total uncertainty of the background. In the case of the pre-fit background uncertainty, the normalisation uncertainty of the tt̄+ ≥ 1b background is not included. – 30 – region, 0.94+0.18 −0.17 in the (τhad τhad , 3j) region, and 0.90 ± 0.26 in the (τhad τhad , ≥4j) re- gion. A similar fit is performed for the tt̄ → W bHu search, yielding B(t → Hu) = −6.5 (stat)−4.2 (syst)] × 10 , assuming B(t → Hc) = 0. The obtained normalisation [−5.3+7.3 +5.3 −4 factors for the fake τhad background agree within 1% with those obtained by the tt̄ → W bHc search. In both cases, the uncertainty of the measured branching ratio is dominated by the statistical uncertainty. The main contributions to the total systematic uncertainty arise from the fake τhad background estimation and the uncertainty associated with the different responses to quark-initiated and gluon-initiated jets. No significant excess of data events above the background expectation is found, and observed (expected) 95% CL lim- its are set on B(t → Hc) and B(t → Hu): B(t → Hc) < 1.9 × 10−3 (2.1 × 10−3 ) and JHEP05(2019)123 B(t → Hu) < 1.7×10−3 (2.0×10−3 ). These results are dominated by the τhad τhad channel, which has a sensitivity a factor of two better than that of the τlep τhad channel. 10.3 Combination of ATLAS searches The tqH(bb̄) and tqH(τ τ ) searches are combined with the ATLAS searches in diphoton [29] and multilepton [30] final states of events in the same data set, referred to as “tqH(γγ) search” and “tqH(ML) search”, respectively. Since all searches, with the exception of the tqH(bb̄) search, are dominated by the data statistical uncertainty, and in each search the dominant systematic uncertainties are different, the combined result is insensitive to the assumed correlations of systematic uncertainties across searches. Therefore, the only systematic uncertainties taken to be fully correlated among the four searches are those affecting the integrated luminosity, the tt̄ cross section, signal modelling, a subset of the uncertainties on the Higgs boson branching ratios (those associated with uncertainties in αS and mb ), and a subset of jet-related uncertainties (jet energy resolution and JVT requirement). The rest of the jet-related uncertainties (jet energy scale and b-tagging) are taken as fully correlated among the tqH(bb̄), tqH(τ τ ), and tqH(ML) searches, but uncorrelated with the tqH(γγ) search. The rest of the uncertainties, e.g. those related to leptons and to background modelling, are taken as uncorrelated among the four searches. The first set of combined results is obtained for each branching ratio separately, setting the other branching ratio to zero. The best-fit combined branching ratios are B(t → Hc) = [3.0+3.0 +2.6 −4 and B(t → Hu) = [4.2+3.2 (stat)+2.6 (syst)] × 10−4 . A −2.7 (stat)−2.1 (syst)] × 10 −2.9 −2.1 comparison of the best-fit branching ratios for the individual searches and their combination is shown in figure 11 for B(t → Hc) and figure 12 for B(t → Hu). The observed (expected) 95% CL combined upper limits on the branching ratios are B(t → Hc) < 1.1 × 10−3 (8.3 × 10−4 ) and B(t → Hu) < 1.2 × 10−3 (8.3 × 10−4 ). A summary of the upper limits on the branching ratios obtained by the individual searches, as well as their combination, is given in table 3 and in figures 13 and 14. Upper limits on the branching ratios B(t → Hq) (q = u, c) can be translated into upper limits on the non-flavour-diagonal Yukawa couplings λtqH appearing in the La- grangian [129]: LFCNC = −λtL qR t̄L qR H − λqL tR q̄L tR H + h.c. – 31 – 5 5 10 10 Events / 0.1 Data Events / 0.1 ATLAS ATLAS Data s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) Fake τhad Fake τhad 104 tqH(ττ) search tt (real τhad) 104 tqH(ττ) search tt (real τhad) τlepτhad, 3j Z→ττ τlepτhad, ≥4j Z→ττ Pre-Fit Other Pre-Fit Other Total Bkg unc. Total Bkg unc. 3 3 10 10 102 102 10 10 JHEP05(2019)123 1.51 1.51 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 BDT discriminant BDT discriminant (a) (b) 5 5 10 10 Events / 0.1 Data Events / 0.1 ATLAS ATLAS Data s = 13 TeV, 36.1 fb-1 tt→WbHc s = 13 TeV, 36.1 fb-1 tt→WbHc Fake τhad Fake τhad 104 tqH(ττ) search Top (real τhad) 104 tqH(ττ) search Top (real τhad) τlepτhad, 3j Z→ττ τlepτhad, ≥4j Z→ττ Post-Fit Other Post-Fit Other Total Pred unc. Total Pred unc. 3 3 10 10 102 102 10 10 1.51 1.51 Data / Pred Data / Pred 1.25 1.25 1 1 0.75 0.75 0.5 0.5 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 BDT discriminant BDT discriminant (c) (d) Figure 9. tqH(τ τ ) search: comparison between the data and prediction for the BDT discriminant distribution in the τlep τhad channel, before and after the fit to data (“Pre-Fit” and “Post-Fit”, respectively) under the signal-plus-background hypothesis. Shown are the (τlep τhad , 3j) region (a) pre-fit and (c) post-fit, and the (τlep τhad , ≥4j) region (b) pre-fit and (d) post-fit. The contributions with real τhad candidates from tt̄, tt̄V , tt̄H, and single-top-quark backgrounds are combined into a single background source referred to as “Top (real τhad )”, whereas the small contributions from Z → `+ `− (` = e, µ) and diboson backgrounds are combined into “Other”. In the pre-fit figures the expected tt̄ → W bHc signal (solid red) corresponding to B(t → Hc) = 1% is also shown, added to the background prediction. In the post-fit figures, the tt̄ → W bHc signal is normalised using the best-fit branching ratio, B(t → Hc) = (−4.4+9.9 −8.5 ) × 10 −4 . The bottom panels display the ratios of data to either the SM background prediction before the fit (“Bkg”) or the total signal- plus-background prediction after the fit (“Pred”). The hashed area represents the total uncertainty of the background. In the case of the pre-fit background uncertainty, the normalisation uncertainty of the fake τhad background is not included. – 32 – Events / 0.1 Data Events / 0.1 ATLAS ATLAS Data s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) s = 13 TeV, 36.1 fb-1 tt→WbHc (ℬ =1%) Fake τhad Fake τhad 10 3 tqH(ττ) search tt (real τhad) 10 3 tqH(ττ) search tt (real τhad) τhadτhad, 3j Z→ττ τhadτhad, ≥4j Z→ττ Pre-Fit Other Pre-Fit Other Total Bkg unc. Total Bkg unc. 102 102 10 10 JHEP05(2019)123 1.51 1.51 Data / Bkg Data / Bkg 1.25 1.25 1 1 0.75 0.75 0.5 0.5 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 BDT discriminant BDT discriminant (a) (b) Events / 0.1 Data Events / 0.1 ATLAS ATLAS Data s = 13 TeV, 36.1 fb-1 tt→WbHc s = 13 TeV, 36.1 fb-1 tt→WbHc Fake τhad Fake τhad 10 3 tqH(ττ) search Top (real τhad) 10 3 tqH(ττ) search Top (real τhad) τhadτhad, 3j Z→ττ τhadτhad, ≥4j Z→ττ Post-Fit Other Post-Fit Other Total Pred unc. Total Pred unc. 102 102 10 10 1.51 1.51 Data / Pred Data / Pred 1.25 1.25 1 1 0.75 0.75 0.5 0.5 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 BDT discriminant BDT discriminant (c) (d) Figure 10. tqH(τ τ ) search: comparison between the data and prediction for the BDT discriminant distribution in the τhad τhad channel, before and after the fit to data (“Pre-Fit” and “Post-Fit”, respectively) under the signal-plus-background hypothesis. Shown are the (τhad τhad , 3j) region (a) pre-fit and (c) post-fit, and the (τhad τhad , ≥4j) region (b) pre-fit and (d) post-fit. The contributions with real τhad candidates from tt̄, tt̄V , tt̄H, and single-top-quark backgrounds are combined into a single background source referred to as “Top (real τhad )”, whereas the small contributions from Z → `+ `− (` = e, µ) and diboson backgrounds are combined into “Other”. In the pre-fit figures the expected tt̄ → W bHc signal (solid red) corresponding to B(t → Hc) = 1% is also shown, added to the background prediction. In the post-fit figures, the tt̄ → W bHc signal is normalised using the best-fit branching ratio, B(t → Hc) = (−4.4+9.9 −8.5 ) × 10 −4 . The bottom panels display the ratios of data to either the SM background prediction before the fit (“Bkg”) or the total signal-plus- background prediction after the fit (“Pred”). The blue triangles indicate points that are outside the vertical range of the figure. The hashed area represents the total uncertainty of the background. In the case of the pre-fit background uncertainty, the normalisation uncertainty of the fake τhad background is not included. – 33 – ATLAS ℬ(t → Hu) = 0 -1 s = 13 TeV, 36.1 fb Total Stat. tot ( stat syst) +2.3 +0.7 +2.2 -3 -0.2 ( -0.7 -2.3 ) × 10 H → bb -2.4 +9.9 +7.7 +6.2 -4.4 ( -7.0 -4.9 ) × 10-4 H→τ τ (τ lepτ had, τ hadτ had) -8.5 +8.0 +6.5 +4.6 H → WW*,τ τ ,ZZ* (2ℓSS, 3ℓ) -0.8 -8.0 ( -6.2 -5.0 ) × 10-4 SM +7.4 +6.8 +2.9 H→γ γ 6.9 -5.4 ( -4.9 -2.4 ) × 10-4 JHEP05(2019)123 +4.0 +3.0 +2.6 3.0 ( -2.7 -2.1 ) × 10-4 Combined -3.4 ×10−3 −2 0 2 4 6 8 ℬ(t → Hc) Figure 11. Summary of the best-fit B(t → Hc) for the individual searches as well as their combination, assuming B(t → Hu) = 0. ATLAS ℬ(t → Hc) = 0 -1 s = 13 TeV, 36.1 fb Total Stat. tot ( stat syst) +2.7 +0.8 +2.5 -3 0.2 ( -0.7 -2.9 ) × 10 H → bb -3.0 +9.1 +7.3 +5.3 -5.3 ( -6.5 -4.2 ) × 10-4 H→τ τ (τ lepτ had, τ hadτ had) -7.8 +7.6 +6.0 +4.6 H → WW*,τ τ ,ZZ* (2ℓSS, 3ℓ) 4.2 -6.7 ( -5.7 -3.5 ) × 10-4 SM +8.1 +6.9 +4.3 H→γ γ 7.5 -5.9 ( -5.7 -1.5 ) × 10-4 +4.2 +3.2 +2.6 4.2 ( -2.9 -2.1 ) × 10-4 Combined -3.6 ×10−3 −2 0 2 4 6 8 ℬ(t → Hu) Figure 12. Summary of the best-fit B(t → Hu) for the individual searches as well as their combination, assuming B(t → Hc) = 0. The branching ratio B(t → Hq) is estimated as the ratio of its partial width [9] to the SM t → W b partial width [130], which is assumed to be dominant. Both predicted partial widths include next-to-leading-order QCD corrections. Using the expression derived in ref. [26], the coupling |λtqH | can be extracted as |λtqH | = (1.92 ± 0.02) B(t → Hq). The p λtqH coupling corresponds to the sum in quadrature of the couplings relative to the two p possible chirality combinations of the quark fields, λtqH ≡ |λtL qR |2 + |λqL tR |2 [129]. The observed (expected) upper limits on the couplings from the combination of the searches are |λtcH | < 0.064 (0.055) and |λtuH | < 0.066 (0.055). A similar set of results can be obtained by simultaneously varying both branching ratios in the likelihood function. Figure 15(a) shows the 95% CL upper limits on the branching – 34 – 95% CL upper limits 95% CL upper limits on B(t → Hc) on B(t → Hu) Observed (Expected) Observed (Expected) H → bb̄ 4.2 × 10−3 (4.0 × 10−3 ) 5.2 × 10−3 (4.9 × 10−3 ) H → τ τ (τlep τhad , τhad τhad ) 1.9 × 10−3 (2.1 × 10−3 ) 1.7 × 10−3 (2.0 × 10−3 ) H → W W ∗ , τ τ, ZZ ∗ (2`SS, 3`) [30] 1.6 × 10−3 (1.5 × 10−3 ) 1.9 × 10−3 (1.5 × 10−3 ) H → γγ [29] 2.2 × 10−3 (1.6 × 10−3 ) 2.4 × 10−3 (1.7 × 10−3 ) Combination 1.1 × 10−3 (8.3 × 10−4 ) 1.2 × 10−3 (8.3 × 10−4 ) Table 3. Summary of 95% CL upper limits on B(t → Hc) and B(t → Hu), in each case neglecting JHEP05(2019)123 the other decay mode. Signatures with two same-charge (three) leptons and no τhad candidates are denoted by 2`SS (3`). H → bb H→τ τ (τ lepτ had, τ hadτ had) ATLAS H → WW*,τ τ ,ZZ* (2ℓSS, 3ℓ) s = 13 TeV, 36.1 fb-1 ℬ(t → Hu) = 0 H→γ γ Observed Expected ± 1σ Combined Expected ± 2σ ×10−3 0 1 2 3 4 5 6 7 8 9 10 95% CL limit on ℬ(t → Hc) Figure 13. 95% CL upper limits on B(t → Hc) for the individual searches as well as their combination, assuming B(t → Hu) = 0. The observed limits (solid lines) are compared with the expected (median) limits under the background-only hypothesis (dotted lines). The surrounding shaded bands correspond to the 68% and 95% CL intervals around the expected limits, denoted by ±1σ and ±2σ, respectively. ratios in the B(t → Hu) versus B(t → Hc) plane. The small differences between the limiting values (on the x- and y-axes) of the branching ratio limits obtained in the two- dimensional scan and those reported in table 3, result from slightly different choices in the tqH(ML) search regarding the final discriminant, which in the two-dimensional case should be common to both signals, and its binning. The corresponding upper limits on the couplings in the |λtuH | versus |λtcH | plane are shown in figure 15(b). 11 Conclusion A search for flavour-changing neutral-current decays of a top quark into an up-type quark (q = u, c) and the Standard Model Higgs boson, t → Hq, is presented. The search is √ based on a dataset of pp collisions at s = 13 TeV recorded in 2015 and 2016 with the – 35 – H → bb H→τ τ (τ lepτ had, τ hadτ had) ATLAS H → WW*,τ τ ,ZZ* (2ℓSS, 3ℓ) s = 13 TeV, 36.1 fb-1 ℬ(t → Hc) = 0 H→γ γ Observed Expected ± 1σ JHEP05(2019)123 Combined Expected ± 2σ ×10−3 0 1 2 3 4 5 6 7 8 9 10 95% CL limit on ℬ(t → Hu) Figure 14. 95% CL upper limits on B(t → Hu) for the individual searches as well as their combination, assuming B(t → Hc) = 0. The observed limits (solid lines) are compared with the expected (median) limits under the background-only hypothesis (dotted lines). The surrounding shaded bands correspond to the 68% and 95% CL intervals around the expected limits, denoted by ±1σ and ±2σ, respectively. −3 2 ×10 0.1 ℬ(t → Hu) |λtuH| 1.8 ATLAS ATLAS 0.09 s=13 TeV, 36.1 fb-1 s=13 TeV, 36.1 fb-1 1.6 0.08 Observed limit 1.4 Observed limit Expected limit ± 1σ 0.07 Expected limit ± 1σ Expected limit ± 2σ 1.2 Expected limit ± 2σ 0.06 All limits at 95% CL All limits at 95% CL 1 0.05 0.8 0.04 0.6 0.03 0.4 0.02 0.2 0.01 −3 0 ×10 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ℬ(t → Hc) |λtcH| (a) (b) Figure 15. 95% CL upper limits (a) on the plane of B(t → Hu) versus B(t → Hc) and (b) on the plane of |λtuH | versus |λtcH | for the combination of the searches. The observed limits (solid lines) are compared with the expected (median) limits under the background-only hypothesis (dotted lines). The surrounding shaded bands correspond to the 68% and 95% CL intervals around the expected limits, denoted by ±1σ and ±2σ, respectively. ATLAS detector at the CERN Large Hadron Collider and corresponding to an integrated luminosity of 36.1 fb−1 . Two complementary analyses are performed to search for top- quark pair events in which one top quark decays into W b and the other top quark decays into Hq, and target the H → bb̄ and H → τ + τ − decay modes, respectively. The tqH(bb̄) search selects events with one isolated electron or muon from the W → `ν decay, and multiple jets, with several of them being identified with high purity as originating from the hadronisation of b-quarks. The tqH(τ τ ) search selects events with either one or two – 36 – hadronically decaying τ -lepton candidates, as well as multiple jets. Both searches employ multivariate techniques to discriminate between the signal and the background on the basis of their different kinematics. No significant excess of events above the background expectation is found, and 95% CL upper limits on the t → Hq branching ratios are derived. In the case of the tqH(bb̄) search, the observed (expected) 95% CL upper limits on the t → Hc and t → Hu branching ratios are 4.2×10−3 (4.0×10−3 ) and 5.2×10−3 (4.9×10−3 ), respectively. In the case of the tqH(τ τ ) search, the observed (expected) 95% CL upper limits on the t → Hc and t → Hu branching ratios are 1.9 × 10−3 (2.1 × 10−3 ) and 1.7 × 10−3 (2.0 × 10−3 ), respectively. The combination of these searches with ATLAS searches in diphoton and multilepton final states yields observed (expected) 95% CL upper JHEP05(2019)123 limits on the t → Hc and t → Hu branching ratios of 1.1 × 10−3 (8.3 × 10−4 ) and 1.2 × 10−3 (8.3 × 10−4 ), assuming B(t → Hu) = 0 and B(t → Hc) = 0 respectively. The corresponding combined observed (expected) upper limits on the |λtcH | and |λtuH | couplings are 0.064 (0.055) and 0.066 (0.055), respectively. Acknowledgments We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Aus- tralia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d’ Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Ger- many; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Ma- jor contributors of computing resources are listed in ref. [131]. – 37 – A Pre-fit and post-fit event yields in the tqH(bb̄) search Table 4 presents the observed and predicted yields in each of the analysis regions for the tqH(bb̄) search before the fit to data. Tables 5 and 6 present the observed and pre- dicted yields in each of the analysis regions after the fit to the data under the signal-plus- background hypothesis, assuming tt̄ → W bHc and tt̄ → W bHu as signal, respectively. 4j, 2b 4j, 3b 4j, 4b tt̄ → W bHc 1990 ± 190 1260 ± 190 24.8 ± 9.5 tt̄ → W bHu 1950 ± 190 1110 ± 170 19 ± 16 tt̄+light-jets 87000 ± 11000 4300 ± 1200 10.2 ± 9.6 JHEP05(2019)123 tt̄+ ≥ 1c 8300 ± 4300 1050 ± 640 3.2 ± 3.3 tt̄+ ≥ 1b 3620 ± 440 2900 ± 580 95 ± 33 tt̄V 176 ± 31 34.8 ± 6.9 2.84 ± 0.74 tt̄H 61.7 ± 9.2 48.7 ± 8.3 5.1 ± 1.0 W +jets 5400 ± 2400 280 ± 130 3.3 ± 1.8 Z+jets 2120 ± 960 115 ± 55 2.4 ± 1.4 Single top 7100 ± 1300 400 ± 120 7.8 ± 6.0 Diboson 267 ± 97 17.2 ± 6.5 0.58 ± 0.27 Multijet 7800 ± 3400 930 ± 360 31 ± 17 Total background 120000 ± 15000 10000 ± 2000 162 ± 44 Data 120572 11275 176 5j, 2b 5j, 3b 5j, ≥4b tt̄ → W bHc 1260 ± 240 1010 ± 190 26.2 ± 8.8 tt̄ → W bHu 1160 ± 240 930 ± 160 23 ± 12 tt̄+light-jets 41300 ± 9100 3200 ± 900 13 ± 11 tt̄+ ≥ 1c 5900 ± 3100 1320 ± 760 21 ± 17 tt̄+ ≥ 1b 3040 ± 250 4300 ± 760 310 ± 83 tt̄V 175 ± 29 67 ± 12 9.1 ± 2.0 tt̄H 81.3 ± 9.5 103 ± 15 18.4 ± 3.5 W +jets 2400 ± 1100 186 ± 89 7.3 ± 3.9 Z+jets 780 ± 350 83 ± 39 6.1 ± 3.8 Single top 2990 ± 780 350 ± 110 16.6 ± 7.6 Diboson 125 ± 56 13.7 ± 6.3 0.89 ± 0.47 Multijet 3700 ± 1500 500 ± 230 3.8 ± 4.9 Total background 60000 ± 11000 10100 ± 1900 405 ± 98 Data 58557 11707 466 ≥ 6j, 2b ≥6j, 3b ≥6j, ≥4b tt̄ → W bHc 760 ± 250 690 ± 210 60 ± 60 tt̄ → W bHu 680 ± 240 570 ± 180 36 ± 40 tt̄+light-jets 22900 ± 8100 2400 ± 910 14 ± 18 tt̄+ ≥ 1c 5300 ± 3000 1800 ± 1100 29 ± 23 tt̄+ ≥ 1b 3270 ± 510 7300 ± 1300 1100 ± 240 tt̄V 229 ± 41 154 ± 30 30.8 ± 6.9 tt̄H 140 ± 18 262 ± 39 71 ± 14 W +jets 1360 ± 630 200 ± 100 15.4 ± 8.2 Z+jets 410 ± 200 63 ± 32 5.1 ± 4.0 Single top 1510 ± 560 360 ± 160 34 ± 20 Diboson 93 ± 47 18.5 ± 9.6 2.1 ± 1.2 Multijet 1920 ± 820 780 ± 360 43 ± 29 Total background 37100 ± 9600 13400 ± 2600 1360 ± 290 Data 35886 14877 1335 Table 4. tqH(bb̄) search: predicted and observed yields in each of the analysis regions considered. The prediction is shown before the fit to data. Also shown are the signal expectations for tt̄ → W bHc and tt̄ → W bHu assuming B(t → Hc) = 1% and B(t → Hu) = 1% respectively. The quoted uncertainties are the sum in quadrature of statistical and systematic uncertainties of the yields, excluding the normalisation uncertainty of the tt̄+ ≥ 1b background, which is determined via a likelihood fit to data. – 38 – 4j, 2b 4j, 3b 4j, 4b tt̄ → W bHc −30 ± 470 −20 ± 300 −0.4 ± 5.9 tt̄+light-jets 82900 ± 4200 4900 ± 500 16 ± 12 tt̄+ ≥ 1c 11400 ± 4800 1360 ± 550 5.9 ± 4.2 tt̄+ ≥ 1b 4270 ± 590 3400 ± 350 110 ± 17 tt̄V 174 ± 28 35.0 ± 5.9 2.69 ± 0.55 tt̄H 62.6 ± 7.8 47.3 ± 6.3 4.68 ± 0.69 W +jets 4800 ± 1800 260 ± 100 2.9 ± 1.3 JHEP05(2019)123 Z+jets 1870 ± 730 102 ± 41 1.9 ± 1.0 Single-top 6360 ± 980 393 ± 96 7.6 ± 5.2 Diboson 242 ± 84 16.3 ± 5.7 0.50 ± 0.22 Multijet 9000 ± 3500 820 ± 240 29 ± 16 Total 121100 ± 2200 11290 ± 280 181 ± 23 Data 120572 11275 176 5j, 2b 5j, 3b 5j, ≥4b tt̄ → W bHc −20 ± 300 −10 ± 240 −0.4 ± 6.2 tt̄+light-jets 38000 ± 3100 3480 ± 460 15.8 ± 9.5 tt̄+ ≥ 1c 8300 ± 3400 2000 ± 760 39 ± 18 tt̄+ ≥ 1b 3410 ± 470 4900 ± 460 356 ± 29 tt̄V 168 ± 26 65 ± 10 8.2 ± 1.4 tt̄H 81.1 ± 8.9 99 ± 12 16.6 ± 2.3 W +jets 2080 ± 820 169 ± 68 6.0 ± 2.8 Z+jets 700 ± 270 74 ± 30 5.6 ± 3.2 Single-top 2560 ± 590 322 ± 90 13.3 ± 5.8 Diboson 111 ± 48 12.5 ± 5.4 0.76 ± 0.39 Multijet 3380 ± 950 560 ± 230 3.6 ± 4.8 Total 58800 ± 1400 11690 ± 360 465 ± 29 Data 58557 11707 466 ≥6j, 2b ≥6j, 3b ≥6j, ≥4b tt̄ → W bHc −10 ± 180 −10 ± 160 −1 ± 14 tt̄+light-jets 20100 ± 2500 2560 ± 490 21 ± 23 tt̄+ ≥ 1c 7800 ± 3300 3000 ± 1100 59 ± 25 tt̄+ ≥ 1b 3390 ± 480 7510 ± 760 1106 ± 83 tt̄V 213 ± 34 145 ± 24 27.0 ± 4.8 tt̄H 134 ± 15 240 ± 30 61.6 ± 8.8 W +jets 1200 ± 470 183 ± 75 12.5 ± 5.7 Z+jets 350 ± 150 56 ± 24 3.5 ± 2.2 Single-top 1220 ± 400 310 ± 120 27 ± 14 Diboson 82 ± 40 16.7 ± 8.2 1.70 ± 0.90 Multijet 1540 ± 530 860 ± 340 37 ± 26 Total 36000 ± 1300 14880 ± 500 1360 ± 72 Data 35886 14877 1335 Table 5. tqH(bb̄) search: predicted and observed yields in each of the analysis regions consid- ered. The background prediction is shown after the fit to data under the signal-plus-background hypothesis (assuming tt̄ → W bHc as signal). The quoted uncertainties are the sum in quadrature of statistical and systematic uncertainties of the yields, computed taking into account correlations among nuisance parameters and among processes. – 39 – 4j, 2b 4j, 3b 4j, 4b tt̄ → W bHu 40 ± 550 20 ± 320 0.4 ± 5.3 tt̄+light-jets 82700 ± 4400 4860 ± 530 15 ± 12 tt̄+ ≥ 1c 11500 ± 5100 1400 ± 580 5.8 ± 4.2 tt̄+ ≥ 1b 4260 ± 590 3400 ± 350 110 ± 17 tt̄V 173 ± 28 34.8 ± 5.8 2.68 ± 0.54 tt̄H 62.4 ± 7.7 47.1 ± 6.2 4.66 ± 0.68 W +jets 4800 ± 1900 260 ± 100 2.9 ± 1.4 JHEP05(2019)123 Z+jets 1880 ± 740 103 ± 42 1.9 ± 1.0 Single-top 6380 ± 990 392 ± 96 7.5 ± 5.2 Diboson 243 ± 85 16.3 ± 5.7 0.50 ± 0.22 Multijet 9000 ± 3500 810 ± 240 29 ± 16 Total 121000 ± 2300 11290 ± 290 181 ± 23 Data 120572 11275 176 5j, 2b 5j, 3b 5j, ≥4b tt̄ → W bHu 20 ± 330 20 ± 270 0.4 ± 6.6 tt̄+light-jets 37800 ± 3400 3450 ± 500 15.8 ± 9.7 tt̄+ ≥ 1c 8400 ± 3700 2000 ± 800 39 ± 19 tt̄+ ≥ 1b 3400 ± 470 4920 ± 460 356 ± 29 tt̄V 168 ± 26 65 ± 10 8.2 ± 1.4 tt̄H 81.0 ± 8.9 99 ± 12 16.6 ± 2.3 W +jets 2100 ± 840 169 ± 69 6.0 ± 2.8 Z+jets 710 ± 280 74 ± 30 5.5 ± 3.2 Single-top 2570 ± 600 320 ± 90 13.4 ± 5.8 Diboson 112 ± 48 12.5 ± 5.5 0.77 ± 0.39 Multijet 3430 ± 990 560 ± 230 3.6 ± 4.8 Total 58800 ± 1500 11690 ± 380 465 ± 29 Data 58557 11707 466 ≥6j, 2b ≥6j, 3b ≥6j, ≥4b tt̄ → W bHu 10 ± 190 10 ± 160 1 ± 10 tt̄+light-jets 20000 ± 2700 2530 ± 520 20 ± 24 tt̄+ ≥ 1c 7900 ± 3600 3000 ± 1200 58 ± 26 tt̄+ ≥ 1b 3390 ± 480 7520 ± 760 1106 ± 83 tt̄V 213 ± 34 147 ± 24 27.0 ± 4.8 tt̄H 135 ± 16 241 ± 30 61.9 ± 9.0 W +jets 1210 ± 480 184 ± 76 12.6 ± 5.8 Z+jets 360 ± 150 57 ± 24 3.6 ± 2.2 Single-top 1240 ± 400 320 ± 120 27 ± 14 Diboson 83 ± 40 16.8 ± 8.3 1.71 ± 0.91 Multijet 1530 ± 530 860 ± 340 37 ± 26 Total 36000 ± 1400 14880 ± 530 1360 ± 73 Data 35886 14877 1335 Table 6. tqH(bb̄) search: predicted and observed yields in each of the analysis regions consid- ered. The background prediction is shown after the fit to data under the signal-plus-background hypothesis (assuming tt̄ → W bHu as signal). The quoted uncertainties are the sum in quadrature of statistical and systematic uncertainties of the yields, computed taking into account correlations among nuisance parameters and among processes. – 40 – B Pre-fit and post-fit event yields in the tqH(τ τ ) search Table 7 presents the observed and predicted yields in each of the analysis regions for the tqH(τ τ ) search before the fit to data. Tables 8 and 9 present the observed and pre- dicted yields in each of the analysis regions after the fit to the data under the signal-plus- background hypothesis, assuming tt̄ → W bHc and tt̄ → W bHu as signal, respectively. τlep τhad , 3j τlep τhad , ≥4j τhad τhad , 3j τhad τhad , ≥4j tt̄ → W bHc 89 ± 14 226 ± 43 46 ± 14 122 ± 32 tt̄ → W bHu 100 ± 17 237 ± 47 32 ± 10 114 ± 28 Fake τhad 2828 ± 78 3200 ± 100 710 ± 110 500 ± 62 JHEP05(2019)123 Top (real τhad ) 3840 ± 720 3160 ± 890 113 ± 72 117 ± 35 Z → ττ 420 ± 140 320 ± 120 283 ± 99 267 ± 96 Other 168 ± 56 103 ± 33 8.9 ± 2.5 11.2 ± 2.5 Total background 7260 ± 730 6770 ± 880 1120 ± 120 900 ± 120 Data 7259 6768 1119 894 Table 7. tqH(τ τ ) search: predicted and observed yields in each of the analysis regions considered. The prediction is shown before the fit to data. Also shown are the signal expectations for tt̄ → W bHc and tt̄ → W bHu assuming B(t → Hc) = 1% and B(t → Hu) = 1% respectively. The contributions with real τhad candidates from tt̄, tt̄V , tt̄H, and single-top-quark backgrounds are combined into a single background source referred to as “Top (real τhad )”, whereas the small contributions from Z → `+ `− (` = e, µ) and diboson backgrounds are combined into “Other”. The quoted uncertainties are the sum in quadrature of statistical and systematic uncertainties of the yields, excluding the normalisation uncertainty of the fake τhad background, which is determined via a likelihood fit to data. τlep τhad , 3j τlep τhad , ≥4j τhad τhad , 3j τhad τhad , ≥4j tt̄ → W bHc −4.2 ± 8.2 −11 ± 21 −2.4 ± 4.3 −10 ± 11 Fake τhad 2290 ± 680 2640 ± 880 640 ± 110 440 ± 100 Top (real τhad ) 4300 ± 670 3660 ± 860 147 ± 84 139 ± 35 Z → ττ 500 ± 100 359 ± 90 320 ± 79 306 ± 76 Other 178 ± 45 112 ± 28 9.6 ± 2.6 12.5 ± 2.6 Total 7230 ± 160 6760 ± 170 1117 ± 65 893 ± 45 Data 7259 6768 1119 894 Table 8. tqH(τ τ ) search: predicted and observed yields in each of the analysis regions considered. The background prediction is shown after the fit to data under the signal-plus-background hypothe- sis (assuming tt̄ → W bHc as signal). The contributions with real τhad candidates from tt̄, tt̄V , tt̄H, and single-top-quark backgrounds are combined into a single background source referred to as “Top (real τhad )”, whereas the small contributions from Z → `+ `− (` = e, µ) and diboson backgrounds are combined into “Other”. The quoted uncertainties are the sum in quadrature of statistical and systematic uncertainties of the yields, computed taking into account correlations among nuisance parameters and among processes. – 41 – τlep τhad , 3j τlep τhad , ≥4j τhad τhad , 3j τhad τhad , ≥4j tt̄ → W bHu −5.7 ± 8.6 −14 ± 21 −2, 0 ± 2.8 −7.1 ± 9.8 Fake τhad 2270 ± 680 2620 ± 880 640 ± 110 440 ± 100 Top (real τhad ) 4320 ± 660 3680 ± 860 148 ± 84 140 ± 35 Z → ττ 470 ± 100 359 ± 89 321 ± 79 308 ± 77 Other 177 ± 44 111 ± 27 9.7 ± 2.6 12.5 ± 2.6 Total 7230 ± 160 6760 ± 160 1118 ± 66 892 ± 45 Data 7259 6768 1119 894 Table 9. tqH(τ τ ) search: predicted and observed yields in each of the analysis regions considered. The background prediction is shown after the fit to data under the signal-plus-background hypothe- sis (assuming tt̄ → W bHu as signal). The contributions with real τhad candidates from tt̄, tt̄V , tt̄H, JHEP05(2019)123 and single-top-quark backgrounds are combined into a single background source referred to as “Top (real τhad )”, whereas the small contributions from Z → `+ `− (` = e, µ) and diboson backgrounds are combined into “Other”. The quoted uncertainties are the sum in quadrature of statistical and systematic uncertainties of the yields, computed taking into account correlations among nuisance parameters and among processes. Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. 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Freundlich45 , D.C. Frizzell125 , D. Froidevaux35 , J.A. Frost132 , C. Fukunaga161 , E. Fullana Torregrosa171 , E. Fumagalli53b,53a , T. Fusayasu114 , J. Fuster171 , O. Gabizon157 , A. Gabrielli23b,23a , A. Gabrielli18 , G.P. Gach81a , S. Gadatsch52 , P. Gadow113 , G. Gagliardi53b,53a , L.G. Gagnon107 , C. Galea27b , B. Galhardo137a,137c , E.J. Gallas132 , B.J. Gallop141 , P. Gallus139 , G. Galster39 , R. Gamboa Goni90 , K.K. Gan123 , S. Ganguly177 , JHEP05(2019)123 J. Gao58a , Y. Gao88 , Y.S. Gao150,n , C. Garcı́a171 , J.E. Garcı́a Navarro171 , J.A. Garcı́a Pascual15a , C. Garcia-Argos50 , M. Garcia-Sciveres18 , R.W. Gardner36 , N. Garelli150 , S. Gargiulo50 , V. Garonne131 , K. Gasnikova44 , A. Gaudiello53b,53a , G. Gaudio68a , I.L. Gavrilenko108 , A. Gavrilyuk109 , C. Gay172 , G. Gaycken24 , E.N. Gazis10 , C.N.P. Gee141 , J. Geisen51 , M. Geisen97 , M.P. Geisler59a , C. Gemme53b , M.H. Genest56 , C. Geng103 , S. Gentile70a,70b , S. George91 , D. Gerbaudo14 , G. Gessner45 , S. 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Kouskoura29 , A.B. Kowalewska82 , R. Kowalewski173 , T.Z. Kowalski81a , C. Kozakai160 , W. Kozanecki142 , A.S. Kozhin121 , V.A. Kramarenko111 , G. Kramberger89 , D. Krasnopevtsev58a , M.W. Krasny133 , A. Krasznahorkay35 , D. Krauss113 , J.A. Kremer81a , J. Kretzschmar88 , P. Krieger164 , K. Krizka18 , K. Kroeninger45 , H. Kroha113 , J. Kroll138 , J. Kroll134 , J. Krstic16 , U. Kruchonak77 , H. Krüger24 , N. Krumnack76 , M.C. Kruse47 , T. Kubota102 , S. Kuday4b , J.T. Kuechler179 , S. Kuehn35 , A. Kugel59a , T. Kuhl44 , V. Kukhtin77 , R. Kukla99 , Y. Kulchitsky105,aj , S. Kuleshov144b , Y.P. Kulinich170 , M. Kuna56 , T. Kunigo83 , A. Kupco138 , T. Kupfer45 , O. Kuprash158 , H. Kurashige80 , L.L. Kurchaninov165a , JHEP05(2019)123 Y.A. Kurochkin105 , A. Kurova110 , M.G. Kurth15d , E.S. Kuwertz35 , M. Kuze162 , J. Kvita127 , T. Kwan101 , A. La Rosa113 , J.L. La Rosa Navarro78d , L. La Rotonda40b,40a , F. La Ruffa40b,40a , C. Lacasta171 , F. Lacava70a,70b , J. Lacey44 , D.P.J. Lack98 , H. Lacker19 , D. Lacour133 , E. Ladygin77 , R. Lafaye5 , B. Laforge133 , T. Lagouri32c , S. Lai51 , S. Lammers63 , W. Lampl7 , E. Lançon29 , U. Landgraf50 , M.P.J. Landon90 , M.C. Lanfermann52 , V.S. Lang44 , J.C. Lange51 , R.J. Langenberg35 , A.J. Lankford168 , F. Lanni29 , K. Lantzsch24 , A. Lanza68a , A. Lapertosa53b,53a , S. Laplace133 , J.F. Laporte142 , T. Lari66a , F. Lasagni Manghi23b,23a , M. Lassnig35 , T.S. Lau61a , A. Laudrain129 , M. Lavorgna67a,67b , M. Lazzaroni66a,66b , B. Le102 , O. Le Dortz133 , E. Le Guirriec99 , E.P. Le Quilleuc142 , M. LeBlanc7 , T. LeCompte6 , F. Ledroit-Guillon56 , C.A. Lee29 , G.R. Lee144a , L. Lee57 , S.C. Lee155 , B. Lefebvre101 , M. Lefebvre173 , F. Legger112 , C. Leggett18 , K. Lehmann149 , N. Lehmann179 , G. Lehmann Miotto35 , W.A. Leight44 , A. Leisos159,w , M.A.L. Leite78d , R. Leitner140 , D. Lellouch177 , K.J.C. Leney92 , T. Lenz24 , B. Lenzi35 , R. Leone7 , S. Leone69a , C. Leonidopoulos48 , G. Lerner153 , C. Leroy107 , R. Les164 , A.A.J. Lesage142 , C.G. Lester31 , M. Levchenko135 , J. Levêque5 , D. Levin103 , L.J. Levinson177 , D. Lewis90 , B. Li15b , B. Li103 , C-Q. Li58a,am , H. Li58a , H. Li58b , L. Li58c , M. Li15a , Q. Li15d , Q.Y. Li58a , S. Li58d,58c , X. Li58c , Y. Li148 , Z. Liang15a , B. Liberti71a , A. Liblong164 , K. Lie61c , S. Liem118 , A. Limosani154 , C.Y. Lin31 , K. Lin104 , T.H. Lin97 , R.A. Linck63 , J.H. Lindon21 , B.E. Lindquist152 , A.L. Lionti52 , E. Lipeles134 , A. Lipniacka17 , M. Lisovyi59b , T.M. Liss170,as , A. Lister172 , A.M. Litke143 , J.D. Little8 , B. Liu76 , B.L Liu6 , H.B. Liu29 , H. Liu103 , J.B. Liu58a , J.K.K. Liu132 , K. Liu133 , M. Liu58a , P. Liu18 , Y. Liu15a , Y.L. Liu58a , Y.W. Liu58a , M. Livan68a,68b , A. Lleres56 , J. Llorente Merino15a , S.L. Lloyd90 , C.Y. Lo61b , F. Lo Sterzo41 , E.M. Lobodzinska44 , P. Loch7 , T. Lohse19 , K. Lohwasser146 , M. Lokajicek138 , J.D. Long170 , R.E. Long87 , L. Longo65a,65b , K.A. Looper123 , J.A. Lopez144b , I. Lopez Paz98 , A. Lopez Solis146 , J. Lorenz112 , N. Lorenzo Martinez5 , M. Losada22 , P.J. Lösel112 , A. Lösle50 , X. Lou44 , X. Lou15a , A. Lounis129 , J. Love6 , P.A. Love87 , J.J. Lozano Bahilo171 , H. Lu61a , M. Lu58a , Y.J. Lu62 , H.J. Lubatti145 , C. Luci70a,70b , A. Lucotte56 , C. Luedtke50 , F. Luehring63 , I. Luise133 , L. Luminari70a , B. Lund-Jensen151 , M.S. Lutz100 , P.M. Luzi133 , D. Lynn29 , R. Lysak138 , E. Lytken94 , F. Lyu15a , V. Lyubushkin77 , T. Lyubushkina77 , H. Ma29 , L.L. Ma58b , Y. Ma58b , G. Maccarrone49 , A. Macchiolo113 , C.M. Macdonald146 , J. Machado Miguens134,137b , D. Madaffari171 , R. Madar37 , W.F. Mader46 , N. Madysa46 , J. Maeda80 , K. Maekawa160 , S. Maeland17 , T. Maeno29 , M. Maerker46 , A.S. Maevskiy111 , V. Magerl50 , D.J. Mahon38 , C. Maidantchik78b , T. Maier112 , A. Maio137a,137b,137d , O. Majersky28a , S. Majewski128 , Y. Makida79 , N. Makovec129 , B. Malaescu133 , Pa. Malecki82 , V.P. Maleev135 , F. Malek56 , U. Mallik75 , D. Malon6 , C. Malone31 , S. Maltezos10 , S. Malyukov35 , J. Mamuzic171 , G. Mancini49 , I. Mandić89 , J. Maneira137a , L. Manhaes de Andrade Filho78a , J. Manjarres Ramos46 , K.H. Mankinen94 , A. Mann112 , A. Manousos74 , B. Mansoulie142 , S. Manzoni66a,66b , A. Marantis159 , G. Marceca30 , L. March52 , L. Marchese132 , G. Marchiori133 , M. Marcisovsky138 , – 55 – C. Marcon94 , C.A. Marin Tobon35 , M. Marjanovic37 , F. Marroquim78b , Z. Marshall18 , M.U.F Martensson169 , S. Marti-Garcia171 , C.B. Martin123 , T.A. Martin175 , V.J. Martin48 , B. Martin dit Latour17 , M. Martinez14,z , V.I. Martinez Outschoorn100 , S. Martin-Haugh141 , V.S. Martoiu27b , A.C. Martyniuk92 , A. Marzin35 , L. Masetti97 , T. Mashimo160 , R. Mashinistov108 , J. Masik98 , A.L. Maslennikov120b,120a , L.H. Mason102 , L. Massa71a,71b , P. Massarotti67a,67b , P. Mastrandrea152 , A. Mastroberardino40b,40a , T. Masubuchi160 , P. Mättig24 , J. Maurer27b , B. Maček89 , S.J. Maxfield88 , D.A. Maximov120b,120a , R. Mazini155 , I. Maznas159 , S.M. Mazza143 , S.P. Mc Kee103 , A. McCarn41 , T.G. McCarthy113 , L.I. McClymont92 , W.P. McCormack18 , E.F. McDonald102 , J.A. Mcfayden35 , G. Mchedlidze51 , M.A. McKay41 , K.D. McLean173 , S.J. McMahon141 , P.C. McNamara102 , C.J. McNicol175 , R.A. McPherson173,ad , J.E. Mdhluli32c , Z.A. Meadows100 , S. Meehan145 , T.M. Megy50 , S. Mehlhase112 , A. Mehta88 , JHEP05(2019)123 T. Meideck56 , B. Meirose42 , D. Melini171,h , B.R. Mellado Garcia32c , J.D. Mellenthin51 , M. Melo28a , F. Meloni44 , A. Melzer24 , S.B. Menary98 , E.D. Mendes Gouveia137a , L. Meng88 , X.T. Meng103 , S. Menke113 , E. Meoni40b,40a , S. Mergelmeyer19 , S.A.M. Merkt136 , C. Merlassino20 , P. Mermod52 , L. Merola67a,67b , C. Meroni66a , F.S. Merritt36 , A. Messina70a,70b , J. Metcalfe6 , A.S. Mete168 , C. Meyer63 , J. Meyer157 , J-P. Meyer142 , H. Meyer Zu Theenhausen59a , F. Miano153 , R.P. Middleton141 , L. Mijović48 , G. Mikenberg177 , M. Mikestikova138 , M. Mikuž89 , M. Milesi102 , A. Milic164 , D.A. Millar90 , D.W. Miller36 , A. Milov177 , D.A. Milstead43a,43b , R.A. Mina150,r , A.A. Minaenko121 , M. Miñano Moya171 , I.A. Minashvili156b , A.I. Mincer122 , B. Mindur81a , M. Mineev77 , Y. Minegishi160 , Y. Ming178 , L.M. Mir14 , A. Mirto65a,65b , K.P. Mistry134 , T. Mitani176 , J. Mitrevski112 , V.A. Mitsou171 , M. Mittal58c , A. Miucci20 , P.S. Miyagawa146 , A. Mizukami79 , J.U. Mjörnmark94 , T. Mkrtchyan181 , M. Mlynarikova140 , T. Moa43a,43b , K. Mochizuki107 , P. Mogg50 , S. Mohapatra38 , S. Molander43a,43b , R. Moles-Valls24 , M.C. Mondragon104 , K. Mönig44 , J. Monk39 , E. Monnier99 , A. Montalbano149 , J. Montejo Berlingen35 , F. Monticelli86 , S. Monzani66a , N. Morange129 , D. Moreno22 , M. Moreno Llácer35 , P. Morettini53b , M. Morgenstern118 , S. Morgenstern46 , D. Mori149 , M. Morii57 , M. Morinaga176 , V. Morisbak131 , A.K. Morley35 , G. Mornacchi35 , A.P. Morris92 , J.D. Morris90 , L. Morvaj152 , P. Moschovakos10 , M. Mosidze156b , H.J. Moss146 , J. Moss150,o , K. Motohashi162 , R. Mount150 , E. Mountricha35 , E.J.W. Moyse100 , S. Muanza99 , F. Mueller113 , J. Mueller136 , R.S.P. Mueller112 , D. Muenstermann87 , G.A. Mullier94 , F.J. Munoz Sanchez98 , P. Murin28b , W.J. Murray175,141 , A. Murrone66a,66b , M. Muškinja89 , C. Mwewa32a , A.G. Myagkov121,ao , J. Myers128 , M. Myska139 , B.P. Nachman18 , O. Nackenhorst45 , K. Nagai132 , K. Nagano79 , Y. Nagasaka60 , M. Nagel50 , E. Nagy99 , A.M. Nairz35 , Y. Nakahama115 , K. Nakamura79 , T. Nakamura160 , I. Nakano124 , H. Nanjo130 , F. Napolitano59a , R.F. Naranjo Garcia44 , R. Narayan11 , D.I. Narrias Villar59a , I. Naryshkin135 , T. Naumann44 , G. Navarro22 , R. Nayyar7 , H.A. Neal103,∗ , P.Y. Nechaeva108 , T.J. Neep142 , A. Negri68a,68b , M. Negrini23b , S. Nektarijevic117 , C. Nellist51 , M.E. Nelson132 , S. Nemecek138 , P. Nemethy122 , M. Nessi35,f , M.S. Neubauer170 , M. Neumann179 , P.R. Newman21 , T.Y. Ng61c , Y.S. Ng19 , Y.W.Y. Ng168 , H.D.N. Nguyen99 , T. Nguyen Manh107 , E. Nibigira37 , R.B. Nickerson132 , R. Nicolaidou142 , D.S. Nielsen39 , J. Nielsen143 , N. Nikiforou11 , V. Nikolaenko121,ao , I. Nikolic-Audit133 , K. Nikolopoulos21 , P. Nilsson29 , H.R. Nindhito52 , Y. Ninomiya79 , A. Nisati70a , N. Nishu58c , R. Nisius113 , I. Nitsche45 , T. Nitta176 , T. Nobe160 , Y. Noguchi83 , M. Nomachi130 , I. Nomidis133 , M.A. Nomura29 , T. Nooney90 , M. Nordberg35 , N. Norjoharuddeen132 , T. Novak89 , O. Novgorodova46 , R. Novotny139 , L. Nozka127 , K. Ntekas168 , E. Nurse92 , F. Nuti102 , F.G. Oakham33,av , H. Oberlack113 , J. Ocariz133 , A. Ochi80 , I. Ochoa38 , J.P. Ochoa-Ricoux144a , K. O’Connor26 , S. Oda85 , S. Odaka79 , S. Oerdek51 , A. Oh98 , S.H. Oh47 , C.C. Ohm151 , H. Oide53b,53a , M.L. Ojeda164 , H. Okawa166 , Y. Okazaki83 , Y. Okumura160 , T. Okuyama79 , A. Olariu27b , L.F. Oleiro Seabra137a , S.A. Olivares Pino144a , D. Oliveira Damazio29 , J.L. Oliver1 , – 56 – M.J.R. Olsson36 , A. Olszewski82 , J. Olszowska82 , D.C. O’Neil149 , A. Onofre137a,137e , K. Onogi115 , P.U.E. Onyisi11 , H. Oppen131 , M.J. Oreglia36 , G.E. Orellana86 , Y. Oren158 , D. Orestano72a,72b , E.C. Orgill98 , N. Orlando61b , A.A. O’Rourke44 , R.S. Orr164 , B. Osculati53b,53a,∗ , V. O’Shea55 , R. Ospanov58a , G. Otero y Garzon30 , H. Otono85 , M. Ouchrif34d , F. Ould-Saada131 , A. Ouraou142 , Q. Ouyang15a , M. Owen55 , R.E. Owen21 , V.E. Ozcan12c , N. Ozturk8 , J. Pacalt127 , H.A. Pacey31 , K. Pachal149 , A. Pacheco Pages14 , L. Pacheco Rodriguez142 , C. Padilla Aranda14 , S. Pagan Griso18 , M. Paganini180 , G. Palacino63 , S. Palazzo48 , S. Palestini35 , M. Palka81b , D. Pallin37 , I. Panagoulias10 , C.E. Pandini35 , J.G. Panduro Vazquez91 , P. Pani35 , G. Panizzo64a,64c , L. Paolozzi52 , T.D. Papadopoulou10 , K. Papageorgiou9,k , A. Paramonov6 , D. Paredes Hernandez61b , S.R. Paredes Saenz132 , B. Parida163 , T.H. Park33 , A.J. Parker87 , K.A. Parker44 , M.A. Parker31 , F. Parodi53b,53a , J.A. Parsons38 , U. Parzefall50 , V.R. Pascuzzi164 , JHEP05(2019)123 J.M.P. Pasner143 , E. Pasqualucci70a , S. Passaggio53b , F. Pastore91 , P. Pasuwan43a,43b , S. Pataraia97 , J.R. Pater98 , A. Pathak178,l , T. Pauly35 , B. Pearson113 , M. Pedersen131 , L. Pedraza Diaz117 , R. Pedro137a,137b , S.V. Peleganchuk120b,120a , O. Penc138 , C. Peng61b , C. Peng15d , H. Peng58a , B.S. Peralva78a , M.M. Perego129 , A.P. Pereira Peixoto137a , L. Pereira Sanchez43b , D.V. Perepelitsa29 , F. Peri19 , L. Perini66a,66b , H. Pernegger35 , S. Perrella67a,67b , V.D. Peshekhonov77,∗ , K. Peters44 , R.F.Y. Peters98 , B.A. Petersen35 , T.C. Petersen39 , E. Petit56 , A. Petridis1 , C. Petridou159 , P. Petroff129 , M. Petrov132 , F. Petrucci72a,72b , M. Pettee180 , N.E. Pettersson100 , A. Peyaud142 , R. Pezoa144b , T. Pham102 , F.H. Phillips104 , P.W. Phillips141 , M.W. Phipps170 , G. Piacquadio152 , E. Pianori18 , A. Picazio100 , R.H. Pickles98 , R. Piegaia30 , J.E. Pilcher36 , A.D. Pilkington98 , M. Pinamonti71a,71b , J.L. Pinfold3 , M. 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Ran15a , T. Rashid129 , S. Raspopov5 , M.G. Ratti66a,66b , D.M. Rauch44 , F. Rauscher112 , S. Rave97 , B. Ravina146 , I. Ravinovich177 , J.H. Rawling98 , M. Raymond35 , A.L. Read131 , N.P. Readioff56 , M. Reale65a,65b , D.M. Rebuzzi68a,68b , A. Redelbach174 , G. Redlinger29 , R. Reece143 , R.G. Reed32c , K. Reeves42 , L. Rehnisch19 , J. Reichert134 , D. Reikher158 , A. Reiss97 , A. Rej148 , C. Rembser35 , H. Ren15d , M. Rescigno70a , S. Resconi66a , E.D. Resseguie134 , S. Rettie172 , E. Reynolds21 , O.L. Rezanova120b,120a , P. Reznicek140 , E. Ricci73a,73b , R. Richter113 , S. Richter44 , E. Richter-Was81b , O. Ricken24 , M. Ridel133 , P. Rieck113 , C.J. Riegel179 , O. Rifki44 , M. Rijssenbeek152 , A. Rimoldi68a,68b , M. Rimoldi20 , L. Rinaldi23b , G. Ripellino151 , B. Ristić87 , E. Ritsch35 , I. Riu14 , J.C. Rivera Vergara144a , F. Rizatdinova126 , E. Rizvi90 , C. Rizzi14 , R.T. Roberts98 , S.H. Robertson101,ad , D. Robinson31 , J.E.M. Robinson44 , A. Robson55 , E. Rocco97 , C. Roda69a,69b , Y. Rodina99 , S. Rodriguez Bosca171 , A. Rodriguez Perez14 , D. Rodriguez Rodriguez171 , A.M. Rodrı́guez Vera165b , S. Roe35 , C.S. Rogan57 , O. Røhne131 , R. Röhrig113 , C.P.A. Roland63 , J. Roloff57 , A. Romaniouk110 , M. Romano23b,23a , N. Rompotis88 , M. Ronzani122 , L. Roos133 , S. Rosati70a , K. Rosbach50 , N-A. Rosien51 , B.J. Rosser134 , E. Rossi44 , E. Rossi72a,72b , E. Rossi67a,67b , L.P. Rossi53b , L. Rossini66a,66b , J.H.N. Rosten31 , R. Rosten14 , M. Rotaru27b , J. Rothberg145 , D. Rousseau129 , D. Roy32c , A. Rozanov99 , Y. Rozen157 , X. Ruan32c , F. Rubbo150 , F. Rühr50 , A. Ruiz-Martinez171 , Z. Rurikova50 , N.A. Rusakovich77 , H.L. Russell101 , – 57 – J.P. Rutherfoord7 , E.M. Rüttinger44,m , Y.F. Ryabov135 , M. Rybar38 , G. Rybkin129 , S. Ryu6 , A. Ryzhov121 , G.F. Rzehorz51 , P. Sabatini51 , G. Sabato118 , S. Sacerdoti129 , H.F-W. Sadrozinski143 , R. Sadykov77 , F. Safai Tehrani70a , P. Saha119 , M. Sahinsoy59a , A. Sahu179 , M. Saimpert44 , M. Saito160 , T. Saito160 , H. Sakamoto160 , A. Sakharov122,an , D. Salamani52 , G. Salamanna72a,72b , J.E. Salazar Loyola144b , P.H. Sales De Bruin169 , D. Salihagic113 , A. Salnikov150 , J. Salt171 , D. Salvatore40b,40a , F. Salvatore153 , A. Salvucci61a,61b,61c , A. Salzburger35 , J. Samarati35 , D. Sammel50 , D. Sampsonidis159 , D. Sampsonidou159 , J. Sánchez171 , A. Sanchez Pineda64a,64c , H. Sandaker131 , C.O. Sander44 , M. Sandhoff179 , C. Sandoval22 , D.P.C. Sankey141 , M. Sannino53b,53a , Y. Sano115 , A. Sansoni49 , C. Santoni37 , H. Santos137a , I. Santoyo Castillo153 , A. Santra171 , A. Sapronov77 , J.G. Saraiva137a,137d , O. Sasaki79 , K. Sato166 , E. Sauvan5 , P. Savard164,av , N. Savic113 , R. Sawada160 , C. Sawyer141 , JHEP05(2019)123 L. Sawyer93,al , C. Sbarra23b , A. Sbrizzi23a , T. Scanlon92 , J. Schaarschmidt145 , P. Schacht113 , B.M. Schachtner112 , D. Schaefer36 , L. Schaefer134 , J. Schaeffer97 , S. Schaepe35 , U. Schäfer97 , A.C. Schaffer129 , D. Schaile112 , R.D. Schamberger152 , N. Scharmberg98 , V.A. Schegelsky135 , D. Scheirich140 , F. Schenck19 , M. Schernau168 , C. Schiavi53b,53a , S. Schier143 , L.K. Schildgen24 , Z.M. Schillaci26 , E.J. Schioppa35 , M. Schioppa40b,40a , K.E. Schleicher50 , S. Schlenker35 , K.R. Schmidt-Sommerfeld113 , K. Schmieden35 , C. Schmitt97 , S. Schmitt44 , S. Schmitz97 , J.C. Schmoeckel44 , U. Schnoor50 , L. Schoeffel142 , A. Schoening59b , E. Schopf132 , M. Schott97 , J.F.P. Schouwenberg117 , J. Schovancova35 , S. Schramm52 , A. Schulte97 , H-C. Schultz-Coulon59a , M. Schumacher50 , B.A. Schumm143 , Ph. Schune142 , A. Schwartzman150 , T.A. Schwarz103 , Ph. Schwemling142 , R. Schwienhorst104 , A. Sciandra24 , G. Sciolla26 , M. Scornajenghi40b,40a , F. Scuri69a , F. Scutti102 , L.M. Scyboz113 , C.D. Sebastiani70a,70b , P. Seema19 , S.C. Seidel116 , A. Seiden143 , T. Seiss36 , J.M. Seixas78b , G. Sekhniaidze67a , K. Sekhon103 , S.J. Sekula41 , N. Semprini-Cesari23b,23a , S. Sen47 , S. Senkin37 , C. Serfon131 , L. Serin129 , L. Serkin64a,64b , M. Sessa58a , H. Severini125 , F. Sforza167 , A. Sfyrla52 , E. Shabalina51 , J.D. Shahinian143 , N.W. Shaikh43a,43b , D. Shaked Renous177 , L.Y. Shan15a , R. Shang170 , J.T. Shank25 , M. Shapiro18 , A.S. Sharma1 , A. Sharma132 , P.B. Shatalov109 , K. Shaw153 , S.M. Shaw98 , A. Shcherbakova135 , Y. Shen125 , N. Sherafati33 , A.D. Sherman25 , P. Sherwood92 , L. Shi155,ar , S. Shimizu79 , C.O. Shimmin180 , Y. Shimogama176 , M. Shimojima114 , I.P.J. Shipsey132 , S. Shirabe85 , M. Shiyakova77 , J. Shlomi177 , A. Shmeleva108 , D. Shoaleh Saadi107 , M.J. Shochet36 , S. Shojaii102 , D.R. Shope125 , S. Shrestha123 , E. Shulga110 , P. Sicho138 , A.M. Sickles170 , P.E. Sidebo151 , E. Sideras Haddad32c , O. Sidiropoulou35 , A. Sidoti23b,23a , F. Siegert46 , Dj. Sijacki16 , J. Silva137a , M. Silva Jr.178 , M.V. Silva Oliveira78a , S.B. Silverstein43a , S. Simion129 , E. Simioni97 , M. Simon97 , R. Simoniello97 , P. Sinervo164 , N.B. Sinev128 , M. Sioli23b,23a , I. Siral103 , S.Yu. Sivoklokov111 , J. Sjölin43a,43b , P. Skubic125 , M. Slater21 , T. Slavicek139 , M. Slawinska82 , K. Sliwa167 , R. Slovak140 , V. Smakhtin177 , B.H. Smart5 , J. Smiesko28a , N. Smirnov110 , S.Yu. Smirnov110 , Y. Smirnov110 , L.N. Smirnova111 , O. Smirnova94 , J.W. Smith51 , M. Smizanska87 , K. Smolek139 , A. Smykiewicz82 , A.A. Snesarev108 , I.M. Snyder128 , S. Snyder29 , R. Sobie173,ad , A.M. Soffa168 , A. Soffer158 , A. Søgaard48 , F. Sohns51 , G. Sokhrannyi89 , C.A. Solans Sanchez35 , M. Solar139 , E.Yu. Soldatov110 , U. Soldevila171 , A.A. Solodkov121 , A. Soloshenko77 , O.V. Solovyanov121 , V. Solovyev135 , P. Sommer146 , H. Son167 , W. Song141 , W.Y. Song165b , A. Sopczak139 , F. Sopkova28b , C.L. Sotiropoulou69a,69b , S. Sottocornola68a,68b , R. Soualah64a,64c,j , A.M. Soukharev120b,120a , D. South44 , S. Spagnolo65a,65b , M. Spalla113 , M. Spangenberg175 , F. Spanò91 , D. Sperlich19 , T.M. Spieker59a , R. Spighi23b , G. Spigo35 , L.A. Spiller102 , D.P. 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Wiedenmann178 , M. Wielers141 , JHEP05(2019)123 C. Wiglesworth39 , L.A.M. Wiik-Fuchs50 , F. Wilk98 , H.G. Wilkens35 , L.J. Wilkins91 , H.H. Williams134 , S. Williams31 , C. Willis104 , S. Willocq100 , J.A. Wilson21 , I. Wingerter-Seez5 , E. Winkels153 , F. Winklmeier128 , O.J. Winston153 , B.T. Winter50 , M. Wittgen150 , M. Wobisch93 , A. Wolf97 , T.M.H. Wolf118 , R. Wolff99 , J. Wollrath50 , M.W. Wolter82 , H. Wolters137a,137c , V.W.S. Wong172 , N.L. Woods143 , S.D. Worm21 , B.K. Wosiek82 , K.W. Woźniak82 , K. Wraight55 , M. Wu36 , S.L. Wu178 , X. Wu52 , Y. Wu58a , T.R. Wyatt98 , B.M. Wynne48 , S. Xella39 , Z. Xi103 , L. Xia175 , D. Xu15a , H. Xu58a,e , L. Xu29 , T. Xu142 , W. Xu103 , Z. Xu150 , B. Yabsley154 , S. Yacoob32a , K. Yajima130 , D.P. Yallup92 , D. Yamaguchi162 , Y. Yamaguchi162 , A. Yamamoto79 , T. Yamanaka160 , F. Yamane80 , M. Yamatani160 , T. Yamazaki160 , Y. Yamazaki80 , Z. Yan25 , H.J. Yang58c,58d , H.T. Yang18 , S. Yang75 , Y. Yang160 , Z. Yang17 , W-M. Yao18 , Y.C. 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Zwalinski35 1 Department of Physics, University of Adelaide, Adelaide; Australia 2 Physics Department, SUNY Albany, Albany NY; United States of America 3 Department of Physics, University of Alberta, Edmonton AB; Canada 4 Department of Physics(a) , Ankara University, Ankara; Istanbul Aydin University(b) , Istanbul; Division of Physics(c) , TOBB University of Economics and Technology, Ankara; Turkey 5 LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 6 High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 7 Department of Physics, University of Arizona, Tucson AZ; United States of America 8 Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 9 Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America 12 Bahcesehir University(a) , Faculty of Engineering and Natural Sciences, Istanbul; Istanbul Bilgi University(b) , Faculty of Engineering and Natural Sciences, Istanbul; Department of Physics (c) , Bogazici University, Istanbul; Department of Physics Engineering(d) , Gaziantep University, Gaziantep; Turkey – 60 – 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 14 Institut de Fı́sica d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 Institute of High Energy Physics(a) , Chinese Academy of Sciences, Beijing; Physics Department(b) , Tsinghua University, Beijing; Department of Physics(c) , Nanjing University, Nanjing; University of Chinese Academy of Science (UCAS)(d) , Beijing; China 16 Institute of Physics, University of Belgrade, Belgrade; Serbia 17 Department for Physics and Technology, University of Bergen, Bergen; Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA; United States of America 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, JHEP05(2019)123 University of Bern, Bern; Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom 22 Centro de Investigaciónes, Universidad Antonio Nariño, Bogota; Colombia 23 Dipartimento di Fisica e Astronomia(a) , Università di Bologna, Bologna; INFN Sezione di Bologna(b) ; Italy 24 Physikalisches Institut, Universität Bonn, Bonn; Germany 25 Department of Physics, Boston University, Boston MA; United States of America 26 Department of Physics, Brandeis University, Waltham MA; United States of America 27 Transilvania University of Brasov(a) , Brasov; Horia Hulubei National Institute of Physics and Nuclear Engineering(b) , Bucharest; Department of Physics(c) , Alexandru Ioan Cuza University of Iasi, Iasi; National Institute for Research and Development of Isotopic and Molecular Technologies(d) , Physics Department, Cluj-Napoca; University Politehnica Bucharest (e) , Bucharest; West University in Timisoara(f ) , Timisoara; Romania 28 Faculty of Mathematics(a) , Physics and Informatics, Comenius University, Bratislava; Department of Subnuclear Physics(b) , Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America 30 Departamento de Fı́sica, Universidad de Buenos Aires, Buenos Aires; Argentina 31 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom 32 Department of Physics(a) , University of Cape Town, Cape Town; Department of Mechanical Engineering Science(b) , University of Johannesburg, Johannesburg; School of Physics(c) , University of the Witwatersrand, Johannesburg; South Africa 33 Department of Physics, Carleton University, Ottawa ON; Canada 34 Faculté des Sciences Ain Chock(a) , Réseau Universitaire de Physique des Hautes Energies — Université Hassan II, Casablanca; Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN)(b) , Rabat; Faculté des Sciences Semlalia(c) , Université Cadi Ayyad, LPHEA-Marrakech; Faculté des Sciences(d) , Université Mohamed Premier and LPTPM, Oujda; Faculté des sciences(e) , Université Mohammed V, Rabat; Morocco 35 CERN, Geneva; Switzerland 36 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France 38 Nevis Laboratory, Columbia University, Irvington NY; United States of America 39 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark 40 Dipartimento di Fisica(a) , Università della Calabria, Rende; INFN Gruppo Collegato di Cosenza (b) , Laboratori Nazionali di Frascati; Italy 41 Physics Department, Southern Methodist University, Dallas TX; United States of America 42 Physics Department, University of Texas at Dallas, Richardson TX; United States of America 43 Department of Physics(a) , Stockholm University; Oskar Klein Centre(b) , Stockholm; Sweden 44 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany 45 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund; Germany – 61 – 46 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany 47 Department of Physics, Duke University, Durham NC; United States of America 48 SUPA — School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom 49 INFN e Laboratori Nazionali di Frascati, Frascati; Italy 50 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany 51 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany 52 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland 53 Dipartimento di Fisica(a) , Università di Genova, Genova; INFN Sezione di Genova(b) ; Italy 54 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany 55 SUPA — School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom 56 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France 57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States JHEP05(2019)123 of America 58 Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics (a) , University of Science and Technology of China, Hefei; Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE) (b) , Shandong University, Qingdao; School of Physics and Astronomy(c) , Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai; Tsung-Dao Lee Institute(d) , Shanghai; China 59 Kirchhoff-Institut für Physik(a) , Ruprecht-Karls-Universität Heidelberg, Heidelberg; Physikalisches Institut(b) , Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany 60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan 61 Department of Physics(a) , Chinese University of Hong Kong, Shatin, N.T., Hong Kong; Department of Physics(b) , University of Hong Kong, Hong Kong; Department of Physics and Institute for Advanced Study(c) , Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 62 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 63 Department of Physics, Indiana University, Bloomington IN; United States of America 64 INFN Gruppo Collegato di Udine(a) , Sezione di Trieste, Udine; ICTP(b) , Trieste; Dipartimento di Chimica(c) , Fisica e Ambiente, Università di Udine, Udine; Italy 65 INFN Sezione di Lecce(a) ; Dipartimento di Matematica e Fisica(b) , Università del Salento, Lecce; Italy 66 INFN Sezione di Milano(a) ; Dipartimento di Fisica(b) , Università di Milano, Milano; Italy 67 INFN Sezione di Napoli(a) ; Dipartimento di Fisica(b) , Università di Napoli, Napoli; Italy 68 INFN Sezione di Pavia(a) ; Dipartimento di Fisica(b) , Università di Pavia, Pavia; Italy 69 INFN Sezione di Pisa(a) ; Dipartimento di Fisica E. Fermi(b) , Università di Pisa, Pisa; Italy 70 INFN Sezione di Roma(a) ; Dipartimento di Fisica(b) , Sapienza Università di Roma, Roma; Italy 71 INFN Sezione di Roma Tor Vergata(a) ; Dipartimento di Fisica(b) , Università di Roma Tor Vergata, Roma; Italy 72 INFN Sezione di Roma Tre(a) ; Dipartimento di Matematica e Fisica(b) , Università Roma Tre, Roma; Italy 73 INFN-TIFPA(a) ; Università degli Studi di Trento(b) , Trento; Italy 74 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck; Austria 75 University of Iowa, Iowa City IA; United States of America 76 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 77 Joint Institute for Nuclear Research, Dubna; Russia 78 Departamento de Engenharia Elétrica(a) , Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora; Universidade Federal do Rio De Janeiro COPPE/EE/IF(b) , Rio de Janeiro; Universidade Federal de São João del Rei (UFSJ)(c) , São João del Rei; Instituto de Fı́sica(d) , Universidade de São Paulo, São Paulo; Brazil 79 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 80 Graduate School of Science, Kobe University, Kobe; Japan 81 AGH University of Science and Technology(a) , Faculty of Physics and Applied Computer Science, Krakow; Marian Smoluchowski Institute of Physics(b) , Jagiellonian University, Krakow; Poland – 62 – 82 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 83 Faculty of Science, Kyoto University, Kyoto; Japan 84 Kyoto University of Education, Kyoto; Japan 85 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 86 Instituto de Fı́sica La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 87 Physics Department, Lancaster University, Lancaster; United Kingdom 88 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 89 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia 90 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 91 Department of Physics, Royal Holloway University of London, Egham; United Kingdom JHEP05(2019)123 92 Department of Physics and Astronomy, University College London, London; United Kingdom 93 Louisiana Tech University, Ruston LA; United States of America 94 Fysiska institutionen, Lunds universitet, Lund; Sweden 95 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne; France 96 Departamento de Fı́sica Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 97 Institut für Physik, Universität Mainz, Mainz; Germany 98 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 100 Department of Physics, University of Massachusetts, Amherst MA; United States of America 101 Department of Physics, McGill University, Montreal QC; Canada 102 School of Physics, University of Melbourne, Victoria; Australia 103 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 104 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 105 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus 106 Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus 107 Group of Particle Physics, University of Montreal, Montreal QC; Canada 108 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia 109 Institute for Theoretical and Experimental Physics (ITEP), Moscow; Russia 110 National Research Nuclear University MEPhI, Moscow; Russia 111 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow; Russia 112 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 113 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 114 Nagasaki Institute of Applied Science, Nagasaki; Japan 115 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 116 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 117 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands 118 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 119 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 120 Budker Institute of Nuclear Physics and NSU(a) , SB RAS, Novosibirsk; Novosibirsk State University Novosibirsk(b) ; Russia 121 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino; Russia 122 Department of Physics, New York University, New York NY; United States of America – 63 – 123 Ohio State University, Columbus OH; United States of America 124 Faculty of Science, Okayama University, Okayama; Japan 125 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 126 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 127 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 128 Center for High Energy Physics, University of Oregon, Eugene OR; United States of America 129 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay; France 130 Graduate School of Science, Osaka University, Osaka; Japan 131 Department of Physics, University of Oslo, Oslo; Norway 132 Department of Physics, Oxford University, Oxford; United Kingdom 133 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris; France JHEP05(2019)123 134 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 135 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg; Russia 136 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 137 Laboratório de Instrumentação e Fı́sica Experimental de Partı́culas — LIP (a) ; Departamento de Fı́sica(b) , Faculdade de Ciências, Universidade de Lisboa, Lisboa; Departamento de Fı́sica (c) , Universidade de Coimbra, Coimbra; Centro de Fı́sica Nuclear da Universidade de Lisboa (d) , Lisboa; Departamento de Fı́sica(e) , Universidade do Minho, Braga; Departamento de Fı́sica Teorica y del Cosmos(f ) , Universidad de Granada, Granada (Spain); Dep Fı́sica and CEFITEC of Faculdade de Ciências e Tecnologia(g) , Universidade Nova de Lisboa, Caparica; Portugal 138 Institute of Physics, Academy of Sciences of the Czech Republic, Prague; Czech Republic 139 Czech Technical University in Prague, Prague; Czech Republic 140 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 141 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 142 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 143 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 144 Departamento de Fı́sica(a) , Pontificia Universidad Católica de Chile, Santiago; Departamento de Fı́sica(b) , Universidad Técnica Federico Santa Marı́a, Valparaı́so; Chile 145 Department of Physics, University of Washington, Seattle WA; United States of America 146 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 147 Department of Physics, Shinshu University, Nagano; Japan 148 Department Physik, Universität Siegen, Siegen; Germany 149 Department of Physics, Simon Fraser University, Burnaby BC; Canada 150 SLAC National Accelerator Laboratory, Stanford CA; United States of America 151 Physics Department, Royal Institute of Technology, Stockholm; Sweden 152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 153 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 154 School of Physics, University of Sydney, Sydney; Australia 155 Institute of Physics, Academia Sinica, Taipei; Taiwan 156 E. Andronikashvili Institute of Physics(a) , Iv. Javakhishvili Tbilisi State University, Tbilisi; High Energy Physics Institute(b) , Tbilisi State University, Tbilisi; Georgia 157 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 158 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 159 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece 160 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan – 64 – 161 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan 162 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 163 Tomsk State University, Tomsk; Russia 164 Department of Physics, University of Toronto, Toronto ON; Canada 165 TRIUMF(a) , Vancouver BC; Department of Physics and Astronomy(b) , York University, Toronto ON; Canada 166 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 167 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 168 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 169 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden JHEP05(2019)123 170 Department of Physics, University of Illinois, Urbana IL; United States of America 171 Instituto de Fı́sica Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia; Spain 172 Department of Physics, University of British Columbia, Vancouver BC; Canada 173 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 174 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 175 Department of Physics, University of Warwick, Coventry; United Kingdom 176 Waseda University, Tokyo; Japan 177 Department of Particle Physics, Weizmann Institute of Science, Rehovot; Israel 178 Department of Physics, University of Wisconsin, Madison WI; United States of America 179 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 180 Department of Physics, Yale University, New Haven CT; United States of America 181 Yerevan Physics Institute, Yerevan; Armenia a Also at Borough of Manhattan Community College, City University of New York, NY; United States of America b Also at California State University, East Bay; United States of America c Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town; South Africa d Also at CERN, Geneva; Switzerland e Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France f Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland g Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain h Also at Departamento de Fı́sica Teorica y del Cosmos, Universidad de Granada, Granada (Spain); Spain i Also at Departamento de Fı́sica, Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal j Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah; United Arab Emirates k Also at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece l Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America m Also at Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom n Also at Department of Physics, California State University, Fresno CA; United States of America o Also at Department of Physics, California State University, Sacramento CA; United States of America – 65 – p Also at Department of Physics, King’s College London, London; United Kingdom q Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia r Also at Department of Physics, Stanford University; United States of America s Also at Department of Physics, University of Fribourg, Fribourg; Switzerland t Also at Department of Physics, University of Michigan, Ann Arbor MI; United States of America u Also at Giresun University, Faculty of Engineering, Giresun; Turkey v Also at Graduate School of Science, Osaka University, Osaka; Japan w Also at Hellenic Open University, Patras; Greece x Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest; Romania y Also at II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany z Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain JHEP05(2019)123 aa Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany ab Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands ac Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest; Hungary ad Also at Institute of Particle Physics (IPP); Canada ae Also at Institute of Physics, Academia Sinica, Taipei; Taiwan af Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan ag Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia ah Also at Instituto de Fı́sica Teórica de la Universidad Autónoma de Madrid; Spain ai Also at Istanbul University, Dept. of Physics, Istanbul; Turkey aj Also at Joint Institute for Nuclear Research, Dubna; Russia ak Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay; France al Also at Louisiana Tech University, Ruston LA; United States of America am Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris; France an Also at Manhattan College, New York NY; United States of America ao Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia ap Also at National Research Nuclear University MEPhI, Moscow; Russia aq Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany ar Also at School of Physics, Sun Yat-sen University, Guangzhou; China as Also at The City College of New York, New York NY; United States of America at Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China au Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia av Also at TRIUMF, Vancouver BC; Canada aw Also at Universita di Napoli Parthenope, Napoli; Italy ∗ Deceased – 66 –