Eur. Phys. J. C (2019) 79:733 https://doi.org/10.1140/epjc/s10052-019-7181-x Regular Article - Experimental Physics Searches for scalar leptoquarks and differential cross-section measurements in dilepton–dijet √ events in proton–proton collisions at a centre-of-mass energy of s = 13 TeV with the ATLAS experiment ATLAS Collaboration CERN, 1211 Geneva 23, Switzerland Received: 1 February 2019 / Accepted: 31 July 2019 / Published online: 3 September 2019 © CERN for the benefit of the ATLAS collaboration 2019 Abstract Searches for scalar leptoquarks pair-produced quark pairs. The lepton can be either electrically charged or √ in proton–proton collisions at s = 13 TeV at the Large neutral. Hadron Collider are performed by the ATLAS experiment. A single Yukawa coupling, λLQ→q , determines the cou- A data set corresponding to an integrated luminosity of pling strength between scalar LQs and the lepton–quark pair 36.1 fb−1 is used. Final states containing two electrons or two [13]. Two additional coupling constants due to magnetic muons and two or more jets are studied, as are states with moment and electric quadrupole moment interactions are one electron or muon, missing transverse momentum and needed for vector LQs [14]. two or more jets. No statistically significant excess above the LQs can be produced both singly and in pairs in proton– Standard Model expectation is observed. The observed and proton ( pp) interactions; diagrams showing representative expected lower limits on the leptoquark mass at 95% confi- single- and pair-production processes are shown in Fig. 1. dence level extend up to 1.29 TeV and 1.23 TeV for first- and The single-LQ production cross-section depends on λLQ→q second-generation leptoquarks, respectively, as postulated in whereas the pair-production cross-section is largely insen- the minimal Buchmüller–Rückl–Wyler model, assuming a sitive to this coupling. For pp interactions with a centre- √ branching ratio into a charged lepton and a quark of 50%. In of-mass energy s = 13 TeV, gluon fusion represents the addition, measurements of particle-level fiducial and differ- dominant pair-production mechanism for LQ masses below ential cross sections are presented for the Z → ee, Z → μμ around 1 TeV. The contribution of the q q̄-annihilation pro- and t t¯ processes in several regions related to the search con- cess rises with LQ mass. Only scalar LQ production is con- trol regions. Predictions from a range of generators are com- sidered in this paper because this is less model dependent pared with the measurements, and good agreement is seen than vector LQ production. The production of vector LQs for many of the observables. However, the predictions for depends on additional parameters and a full interpretation in the Z →  measurements in observables sensitive to jet such models is beyond the scope of this analysis. The results energies disagree with the data. obtained here can, however, be regarded as conservative esti- mates of limits on vector LQ production, since the production cross-section for vector LQs is typically much larger than for 1 Introduction scalar LQs, while the kinematic properties used to search for their signature are very similar for both spin hypotheses [8]. Leptoquarks (LQs) are features of a number of extensions of The benchmark signal model used in this paper is the the Standard Model (SM) [1–8] and may provide an explana- minimal Buchmüller–Rückl–Wyler (BRW) model [15]. This tion for the similarities between the quark and lepton sectors model imposes a number of constraints on the properties in the SM. They also appear in models addressing some of of the LQs. Couplings are purely chiral and LQs belong to the recent b-flavour anomalies [9–11]. They are colour-triplet three families, corresponding to the three SM generations, bosons with fractional electric charge and possess non-zero such that only leptons and quarks within a given genera- baryon and lepton number [12]. Scalar and vector LQs have tion can interact. The requirement of same-generation inter- been proposed and are expected to decay directly into lepton– actions excludes flavour-changing neutral currents (FCNC) [16]. The branching ratio (BR) of a LQ decay into different states is taken as a free parameter. In this paper, β denotes  e-mail: atlas.publications@cern.ch 123 733 Page 2 of 45 Eur. Phys. J. C (2019) 79:733 Fig. 1 Diagrams for representative a single and b pair production of LQs q l g LQ g g LQ LQ (a) (b) the BR for a LQ decay into a charged lepton and quark, a simultaneous background-only profile likelihood fit to three LQ → ± q. The BR for a LQ decay into a neutrino and data control regions (CRs). The CRs are defined with specific quark is (1 − β). selections such that they are orthogonal to the signal regions This paper presents a search for LQs pair-produced in (SRs), free from any LQ signal of interest, and enriched in √ pp collisions at s = 13 TeV, performed by the ATLAS their relevant background process. For the particular bench- experiment at the Large Hadron Collider (LHC). In previ- mark model chosen, further signal and background discrim- ous searches for pair-produced LQs made by ATLAS [17– ination could be achieved by exploiting flavour tagging in 21], most recently with 3.2 fb−1 of data collected at order to reject events with b-hadrons in the search region. √ s = 13 TeV, the existence of scalar LQs with masses below This is not used, in order to maintain sensitivity to a LQ cou- 1100 (900) GeV for first-generation LQs at β = 1 (0.5) and, for pling to a b-quark. The shapes and normalisations of other second-generation LQs, with masses below 1050 (830) GeV (subdominant) backgrounds are estimated from MC simu- at β = 1 (0.5) was excluded [21]. Searches have also been lation. Background contributions from misidentified leptons made by the CMS Collaboration [22–28] using the same are also sub-dominant, and are estimated from data when model assumptions as in this work. That experiment found their contribution is significant. that scalar LQs with masses below 1435 (1270) GeV for first- Several variables that provide separation between signal generation LQs at β = 1 (0.5) and below 1530 (1285) GeV and background processes are combined into a single dis- for second-generation LQs at β = 1 (0.5) are excluded at 95% criminant using a boosted decision tree (BDT) method [35]. confidence level (CL) using a data sample of 35.9 fb−1 col- For each LQ-mass hypothesis and for each lepton flavour, √ lected at s = 13 TeV [27,28]. An overview of limits on LQ two BDTs are used: one for the dilepton channel and one production and masses can be found in Ref. [29]. for the lepton–neutrino channel. The BDT output score is The decay of pair-produced LQs can lead to final states binned and the signal is accumulated at high score values. A containing a pair of charged leptons and jets, or one charged single bin at a high score defines the SR for the respective lepton, a neutrino and jets. For down-type leptoquarks with mass hypothesis. The bin range is chosen such that a sensi- a charge of 13 e, assumed here as a benchmark, this can be tivity estimator based on the expected number of signal and written more explicitly as LQLQ −→ − q+ q̄ for the dilep- background events is maximised. ton channel, and LQLQ −→ − q ν̄ q¯ or LQLQ −→ νq+ q¯ A profile likelihood fit is performed simultaneously to for the lepton-neutrino channel, respectively. The four search both the dilepton and lepton-neutrino channel for each lep- channels in this paper correspond to the above decays. The ton flavour to extract a limit on the signal yield. This is done eejj (μμjj) channel comprises exactly two electrons (muons) for different assumptions of LQ mass and branching ratio β, and at least two jets, and the eνjj (μνjj) channel requires resulting in exclusion bounds in terms of these parameters. exactly one electron (muon), missing momentum in the trans- In addition to the search results, this paper presents a set of verse plane and at least two jets. The electron and muon particle-level fiducial and differential cross-section measure- channels are treated separately as they correspond to LQs ments in six dilepton–dijet regions which are related to the of different generations. The dilepton and lepton–neutrino analysis CRs. The measurements are made for the dominant channels are combined in order to increase the sensitivity process in each region (Z +jets or t t¯) exclusively. The moti- to the parameter of the BRW model, β. While the main tar- vation for making these measurements is as follows. Search get of this paper are first- and second-generation LQs, there CRs are typically in kinematic regions which are not directly are also dedicated searches looking for 3rd generation LQ covered by dedicated measurements of SM processes. For production [20,30–34]. instance, the ATLAS measurement of Z + jets production The main background processes in this search are described in Ref. [36] has much looser selections on the trans- Z /γ ∗ +jets, W +jets and t t¯ production. The shapes of these verse momentum ( pT ) of leptons and jets than the region that background distributions are estimated from Monte Carlo includes Z bosons for this search. Event generators are tuned (MC) simulations while their normalisation is obtained from and validated to ensure that they agree with the available 123 Eur. Phys. J. C (2019) 79:733 Page 3 of 45 733 SM data. However, in some cases, although good agreement netic calorimeters are deployed, with an additional thin LAr is seen for the regions covered by dedicated SM measure- presampler covering |η| < 1.8, to correct for energy loss in ments, significant disagreements remain possible in more material upstream of the calorimeters. Hadronic calorimetry extreme regions where no measurements are available to vali- is provided by a steel/scintillator-tile calorimeter, segmented date the generator predictions. Indeed, although no disagree- into three barrel structures within |η| < 1.7, and two cop- ment was seen when validating generated Z +jets samples per/LAr hadronic endcap calorimeters. The forward region against various SM measurements [37], significant disagree- (3.1 < |η| < 4.9) is instrumented with a LAr calorimeter with ment was observed in the Z +jets CR for this search. Extract- copper (electromagnetic) and tungsten (hadronic) absorbers. ing particle-level cross-sections in these CRs and related Surrounding the calorimeters is a muon spectrometer (MS) more extreme regions of phase space can therefore provide with air-core toroid magnets to provide precise muon identi- complementary information to validate the performance of fication and momentum measurements, consisting of a sys- event generators. Such measurements may lead to improved tem of precision tracking chambers providing coverage over background modelling in future searches with final states |η| < 2.7, and detectors with triggering capabilities over using similar CRs (see for example Ref. [38]). |η| < 2.4. A two-level trigger system [40], the first level using The paper is organised as follows. Section 2 gives an custom hardware and followed by a software-based level, is overview of the ATLAS detector, while Sect. 3 summarises used to reduce the event rate to a maximum of around 1 kHz information on the data set and simulations used. The physics for offline storage. objects used in the analysis are defined in Sect. 4. The back- ground estimation and CRs used in the process are described in Sect. 5, and Sect. 6 provides details on the cross-section 3 Data and Monte Carlo samples extraction. Section 7 gives an overview over the multivariate analysis employed for the search and the SRs defined with it. 3.1 Data sample The systematic uncertainties are summarised in Sect. 8, and the statistical procedure employed for the search is described This analysis is based on proton–proton collision data at a √ in Sect. 9. Results from both the measurement and the search centre-of-mass energy of s = 13 TeV, collected at the LHC are given in Sect. 10. Section 11 concludes the paper. during 2015 and 2016. After imposing requirements based on beam and detector conditions and data quality, the data sam- ple corresponds to an integrated luminosity of 36.1 fb−1 . A 2 The ATLAS detector set of triggers is used to select events [40–42]. For the dielec- tron channel, a two-electron trigger with a transverse energy The ATLAS experiment [39] is a multipurpose detector with (E T ) threshold of 17 GeV for each electron was used for the a forward–backward symmetric cylindrical geometry and entire data set. In the electron–neutrino channel, events were nearly 4π coverage in solid angle.1 The three major sub- selected by either of two single-electron triggers, as described components are the tracking detector, the calorimeter and below. For the 2015 data set, one trigger required E T above the muon spectrometer. Charged-particle tracks and vertices 60 GeV. A second trigger with a higher threshold of 120 GeV are reconstructed by the inner detector (ID) tracking sys- but looser identification requirements otherwise was used. tem, comprising silicon pixel and microstrip detectors cov- For the 2016 data set, the main difference is that the second ering the pseudorapidity range |η| < 2.5, and a transition trigger had a threshold of 140 GeV. The trigger efficiency is radiation straw-tube tracker that covers |η| < 2.0 and pro- at least 95% for electrons above threshold for the kinematic vides electron identification. The ID is immersed in a homo- region used in this work. geneous 2 T magnetic field provided by a solenoid. Elec- In the muon channel, the same triggers were used for the tron, photon, and jet energies are measured with sampling μμjj and the μνjj channels. Events were selected by either calorimeters. The calorimeter system covers a pseudorapid- of two single-muon triggers. For the 2015 data, one trig- ity range of |η| < 4.9. Within the region |η| < 3.2, barrel and ger required a transverse momentum of at least 26 GeV and endcap high-granularity lead/liquid argon (LAr) electromag- applied an isolation criterion. To maintain a high efficiency at high pT , a second trigger with a threshold of 50 GeV and no 1 ATLAS uses a right-handed coordinate system with its origin at the further requirements was used. The main difference for the nominal interaction point (IP) in the centre of the detector and the z-axis 2016 data set is that slightly different isolation requirements along the beam pipe. The x-axis points from the IP to the centre of the were used for the lower- pT trigger. The thresholds were the LHC ring, and the y-axis points upward. Cylindrical coordinates (r, φ) same as in 2015. For the offline pT values considered in this are used in the transverse plane, φ being the azimuthal angle around the analysis, the trigger efficiencies have reached their plateau beam pipe. The pseudorapidity is defined in terms of the polar angle θ as η = − ln tan(θ/2). The quantity R = ( φ)2 + ( η)2 is used to values, which vary between 50 and 80% in the more central define a cone size. detector region and are around 90% for |η| > 1.05. The vari- 123 733 Page 4 of 45 Eur. Phys. J. C (2019) 79:733 ation in trigger efficiency is due to local inefficiencies and single-top-quark events were produced with the Powheg- incomplete detector coverage. Box v1 generator. For the generation of single-top samples, Multiple pp interactions in the same or neighbouring the UEPS modelling was done using Pythia 6.428 [62] with bunch-crossings can lead to many reconstructed vertices in the CTEQ6L1 [63] PDF sets and Perugia 2012 [64] tune. the beam collision of interest. The primary vertex of the event, The value of the top mass (m t ) was set to 172.5 GeV. The from which the leptons are required to originate, is defined as EvtGen v1.2.0 [65] program was used for properties of the that with the largest sum of squared transverse momenta of bottom and charm hadron decays. its associated tracks. Selected events must contain a primary Diboson processes with at least one charged lepton in vertex with at least two associated tracks with pT > 0.4 GeV. the final state were simulated using the Sherpa 2.1.1 gen- erator. All diagrams with four electroweak vertices were 3.2 Signal and background simulations considered. They were calculated for up to one parton at NLO and up to three partons at LO using the Comix and Samples of simulated events with pair-produced scalar OpenLoops matrix element generators and merged with the LQs with masses between 200 and 1700 GeV, in steps Sherpa UEPS model using the ME+PS@NLO prescription. of 50 GeV up to 1500 GeV and thereafter 100 GeV, The CT10 PDF set was used in conjunction with dedicated were generated at next-to-leading order (NLO) in QCD parton shower tuning developed by the Sherpa authors. [43–45] with the MadGraph 2.4.3 [43,46] program using The generator cross sections, calculated up to NLO, were MadSpin [47] for the decay of LQs. In the simulation, lep- used. toquarks with a charge of 13 e are used. Accordingly, the first To model the effect of multiple proton–proton interac- generation LQ decays to either a u-quark and an electron, or tions in the same or neighbouring bunches (pile-up), simu- a d-quark and an electron neutrino. The second generation lated inclusive proton–proton events were overlaid on each LQ decays to either a c-quark and a muon or an s-quark and generated signal and background event. The pile-up was a muon neutrino. The anti-leptoquarks decay into the corre- simulated with Pythia 8.186 using tune A2 [66] and the sponding anti-particles. The generator output was interfaced MSTW2008LO PDF set [67]. Simulated events were cor- with Pythia 8.212 [48] for the event simulation beyond the rected using per-event weights to describe the distribution of hard scattering process, i.e. the parton shower, hadronisation the average number of interactions per proton bunch-crossing and underlying event, collectively referred to as UEPS. The as observed in data. A14 set of tuned parameters (tune) [49] was used for the The detector response to events in the SM background UEPS modelling. The NNPDF3.0 NLO [50] parton distri- samples was evaluated with the Geant4-based detector sim- bution function (PDF) set was used. The coupling λLQ→q , ulation [68,69]. A fast simulation employing a parameter- which determines the LQ lifetime and width [13] was set to √ isation of calorimeter response [70] and Geant4 for the 4π α, where α is the fine-structure constant. This value cor- other detector components was used for the signal samples. responds to a LQ full width of about 0.2% of its mass; LQs The standard ATLAS reconstruction software was used for can thus be considered to decay promptly. Samples were gen- both simulated and pp data. Furthermore, correction fac- erated for β with a value of 0.5. tors, termed scale factors, which were derived from data Events containing W or Z bosons and associated jets [51] were applied as event weights to the simulation of the lepton were simulated with the Sherpa 2.2.1 generator [52]. These trigger, reconstruction, identification, isolation, and impact event samples include off-shell production of the bosons. parameter selection, and of b-tagging efficiencies. Matrix elements were calculated in perturbative QCD for up to two partons at NLO and four partons at leading order (LO) using the Comix [53] and OpenLoops [54] matrix ele- 4 Object definition and event pre-selection ment generators. Merging with the Sherpa UEPS model was performed with the ME+PS@NLO method [55]. The Electrons are reconstructed from ID tracks which are matched NNPDF3.0 PDF set [56] at next-to-next-to-leading order to energy clusters found in the electromagnetic calorimeter. (NNLO) was used. Simulated processes in which a Z boson The reconstruction efficiency is higher than 97% for candi- decays into leptons are hereafter termed Drell–Yan processes. dates with pT greater than 30 GeV. An object is identified For the simulation of t t¯ and single-top-quark production in as an electron following requirements made on the quality the W t final state and s-channel the Powheg- Box v2 [57– of the associated track, shower shapes exploiting the lon- 60] generator was used. For the UEPS modelling for the t t¯ gitudinal segmentation of the electromagnetic calorimeter, samples, Pythia 8.210 was used, with the A14 tune and leakage into the hadronic calorimeter, the quality of the the NNPDF2.3 LO [61] PDF set. The t t¯ samples used the track-to-cluster matching and measurements of transition NNPDF3.0 set in the matrix element calculations, the single- radiation made with the TRT [71]. The transverse energy top samples used the CT10 PDF set. Electroweak t-channel of the electron candidates in the eej j (eν j j) channel must 123 Eur. Phys. J. C (2019) 79:733 Page 5 of 45 733 exceed 40 (65) GeV. Only electron candidates in the pseu- The missing transverse momentum is defined as the dorapidity region |η| < 2.47 and excluding the transition negative vector transverse momentum sum of the recon- region between the barrel and endcap EM calorimeters structed and calibrated physics objects, plus an additional (1.37 < |η| < 1.52) are used. The electron identifica- soft term [81,82]. The soft term is built from tracks that are tion uses a multivariate technique to define different work- not associated with any reconstructed electron, muon or jet, ing points of selection efficiency. In the dielectron channel, but which are associated with the primary vertex. The abso- the ‘medium’ identification working point with an efficiency lute value of the missing transverse momentum is denoted above 90% for the candidates considered in this analysis is by E Tmiss . In the ν j j channel,  an E Tmiss -related quantity, cal- used. To achieve better rejection against jets misidentified as culated as S = E Tmiss / pT + pT + pT is used to further j1 j2 electrons, a tighter selection is used in the electron–neutrino reduce backgrounds from objects wrongly reconstructed as channel. For the electron–neutrino channel, the ‘tight’ iden- j1 j2 leptons. Here, pT ( pT ) is the pT of the leading (subleading) tification working point is used with an efficiency of 85% jet and pT is the pT of the charged lepton. or more for candidates with pT above 65 GeV. The elec- Ambiguities in the object identification which arise during trons are required to be isolated, using both calorimeter- and reconstruction, i.e. when a reconstructed object can match track-based criteria such that the isolation efficiency is 98%. multiple object hypotheses (electron, muon, jet), are resolved Finally, the electron candidates have to be compatible with in several steps. First, electrons are removed if they share originating from the primary vertex. a track with a muon. An algorithm is also used to remove Muon tracks are reconstructed independently in the ID jet–lepton ambiguities based on the proximity of identified and the MS [72]. Tracks are required to have a minimum leptons and jets. number of hits in each system, and must be compatible in For the dilepton channel, events are selected such that terms of geometrical and momentum matching. Information they contain at least two jets and two same-flavour leptons from both the ID and MS is used in a combined fit to refine fulfilling the requirements above for the respective flavour. the measurement of the momentum of each muon over |η| < Selected lepton-neutrino events have to contain at least two 2.5 [73]. The efficiency for reconstructing muons is 98%. jets, one lepton, and a missing transverse energy of more than As for the electron channels, muon candidates are required 40 GeV. to have pT > 40 GeV and pT > 65 GeV for the μμjj and μνjj searches respectively. They are further required to be compatible with originating from the primary vertex. A track- based isolation requirement is applied to the muons, yielding 5 Control regions and background estimation a selection efficiency of 99%. Jets are reconstructed using the anti-kt algorithm [74] The major SM background processes to the LQ signal cor- with a radius parameter R = 0.4 from topological clus- respond to the production of (off-shell) Z /γ ∗ +jets, W +jets ters of calorimeter cells which are noise-suppressed and cali- and t t¯ events in which at least one top quark decays lep- brated to the electromagnetic scale. They are calibrated using tonically. These backgrounds are determined using data in energy- and η-dependent correction factors derived from selected CRs, as described below. Subdominant contributions simulation and with residual corrections from in situ mea- arising from diboson, single-top, W → τ ν and Z → τ τ surements [75]. The jets must satisfy pT > 60 GeV and production are estimated entirely from simulation. There are |η| < 2.5. Additional jet quality criteria are also applied also background contributions due to objects being misiden- to remove fake jets caused by detector effects [76]. Further- tified as leptons or non-prompt leptons that are produced in more, to eliminate jets containing a large energy contribution the decay of hadrons. These backgrounds are collectively from pile-up, jets are tested for compatibility with the hard referred to as the fake (lepton) background and are estimated scatter vertex with a jet vertex tagger discriminant, utilising in a data-driven way where relevant. information from the ID tracks associated with the jet [77]. Three CRs, independent of the SRs used for the leptoquark Jets containing b-hadrons (b-jets) are identified with an search (see Sect. 7), in which any high-mass LQ signal con- algorithm which is based on multivariate techniques. The tributions are expected to be negligible are defined in order to algorithm combines information from the impact parame- estimate the main backgrounds. First, the Z /γ ∗ +jets control ters of displaced tracks and from topological properties of region (Z CR) in the dilepton channels is defined by restrict- secondary and tertiary decay vertices reconstructed within ing the dilepton invariant mass, m  , to values between 70 and the jet. A working point at which the b-tagging efficiency is 110 GeV. These CRs contain samples of Z /γ ∗ +jets events around 77% for jets originating from a b-quark is chosen, as of purity 92% (94%) for the electron (muon) channel. Next, determined with a MC simulation of t t¯ processes [78,79]. Scale factors needed to match the MC performance to data are compatible with unity [80]. 123 733 Page 6 of 45 Eur. Phys. J. C (2019) 79:733 the W control region (W CR) in the lepton–neutrino chan- This analysis makes use of the matrix method [83] to esti- nels requires the transverse mass,2 m T , to be between 40 and mate the background from misidentified electrons for the 130 GeV. In addition, E Tmiss has to be greater than 40 GeV first-generation LQ search. This background is negligible for and the observable S greater than 4. Events that contain one the second generation search. The matrix method is based or more b-jets are vetoed to reduce the t t¯ contribution. The on the estimation of the probabilities for prompt electrons purity of the W CR is 74% (80%) for the electron (muon) and fake electron candidates that pass identification and iso- channel. lation selections that are looser than the nominal selection To improve the description of data, a reweighting of described in Sect. 4, to also pass the nominal selection. These the Z /γ ∗ +jets and W +jets predictions by Sherpa 2.2.1 probabilities are referred to as the prompt rate and fake rate, is performed. The weights in this procedure are parame- respectively. In the loose selection, several criteria that are terised as a function of the dijet mass, m j j , in the respec- used to suppress fake contributions in the nominal selection tive CR, and applied to both CRs and SRs (see Sect. 7). are relaxed. In particular, no isolation is required in the loose The parameterisation is derived by fitting the ratio between selection. The prompt rate is estimated to a good approxima- data and prediction with second-degree polynomials. Fig- tion from MC simulations, while the fake rate is estimated ure 2 shows distributions of m j j and leading jet pT in the from a data sample enriched in fake backgrounds. To sup- Z CR for the μμjj channel before and after reweighting. press contributions from prompt electrons in this sample, The data are better described by the Monte Carlo simula- events are rejected if they contain at least two electron can- tions following this reweighting. The total uncertainty (sta- didates that pass the ‘medium’ identification criteria. In both tistical, systematic and theory) is shown. The total uncer- channels, events are also rejected if there are two electron tainty for CR distributions is dominated by the uncertainty candidates that fulfil the ‘loose’ object definition specific to in the theoretical predictions which is correlated between the respective channel and have an invariant mass in an inter- bins. Experimental uncertainties from jet energy scale and val of ±20 GeV around the Z boson mass. The contribution jet energy resolution are typically around 1%. Similarly, the from the fake electron background is at most 24%, but gen- Sherpa 2.2.1 predictions of W +jets require reweighting erally much less. to match the data. The functional form of the reweighting Backgrounds with non-prompt muons arise from decays algorithm is similar to that used for the Z /γ ∗ +jets sample. of heavy-flavour hadrons inside jets. Their contribution to the The signal regions differ from the control regions mainly by dimuon final states is negligible as in the search documented the cut on m  or m T . It is verified that applying weights in Ref. [84]. In lepton–neutrino final states, a contamination derived in one mass region to the MC simulation in a dif- of about 5% was found [83], corresponding to about half the ferent mass region provides a good description of the data size of the fake electron background. Tests showed that a in that region, indicating that the same correction is also contamination of 5% would not alter the results of this anal- valid in the signal region. All mass regions used in this test ysis, and therefore this background is neglected. The uncer- do not overlap with the signal regions. A systematic uncer- tainty in the total background estimation due to neglecting tainty is included to account for the reweighting procedure this background is taken to be half the size of the background (Sect. 8). from fake electrons. In the statistical method used to evaluate the exclusion In Fig. 3, the distributions of m min LQ and the dilepton invari- limits which is described in Sect. 9, the normalisations ant mass in the Z CR are shown for the μμjj and eejj chan- of background estimates are adjusted in the fitting proce- nels. The quantities m min max LQ ane m LQ are defined as the lower dure. These normalisations are not applied to any of the and higher of the two invariant masses which can be recon- distributions shown prior to that section. Finally, the t t¯ structed using the two lepton–jet pairs in the dilepton chan- control region (t t¯ CR) for the μνjj and eνjj channels is nels. In the analysis, the lepton–jet pairing is chosen such defined with the same requirements on m T and E Tmiss as that the absolute mass difference between the two LQ candi- the W CR, but here the presence of at least two b-jets dates is minimised. The full set of experimental and theoret- is required. The purity of the t t¯ CR is 86% (89%) for ical uncertainties is considered for the uncertainty band. The the electron (muon) channel. The CRs are summarised in spectra correspond to the predictions prior to the fit described Table 1. The Z CRs and other measurement regions are in Sect. 9. used to extract particle-level differential cross-sections (see Distributions for the eνjj and μνjj channels are similarly Sect. 6). shown for the W and t t¯ CRs in Figs. 4 and 5, respectively. The E Tmiss and m T spectra are shown in Fig. 4, while the m j j and m T spectra are shown in Fig. 5. The data and the 2 The  transverse mass is defined as predictions in the various CRs are in agreement within uncer- m T = 2 · pT · E Tmiss · (1 − cos( φ(, E Tmiss ))) where φ(, E Tmiss ) tainties (described in Sect. 8) following the reweighting of is the azimuthal separation between the charged lepton and the E Tmiss . the Sherpa 2.2.1 predictions for Z /γ ∗ +jets and W +jets 123 Eur. Phys. J. C (2019) 79:733 Page 7 of 45 733 105 Events / 200 GeV Events / 200 GeV ATLAS 105 ATLAS 104 13 TeV, 36.1 fb-1 4 13 TeV, 36.1 fb-1 μ μ jj, Z CR 10 μ μ jj, Z CR 103 Data Z(μ μ ) Data Z(μ μ ) tt VV 103 tt VV 2 tW tot. unc. tW tot. unc. 10 102 10 10 1 1 10−1 −1 10−2 10 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 2000 4000 6000 8000 0 500 1000 1500 2000 j, lead m jj [GeV] pT [GeV] (a) (b) Events / 200 GeV Events / 200 GeV ATLAS 105 ATLAS 104 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 μ μ jj, Z CR 104 μ μ jj, Z CR 103 Data Z(μ μ ) Data Z(μ μ ) tt VV 103 tt VV 102 tW tot. unc. tW tot. unc. 102 10 10 1 1 10−1 10−2 10−1 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 2000 4000 6000 8000 0 500 1000 1500 2000 j, lead m jj [GeV] p [GeV] T (c) (d) Fig. 2 Distributions of m j j (left) and leading jet pT (right) in the Z panels show the ratio of data to expected background. The grey hatched CR for the μμjj channel. Data are shown together with MC contribu- band represents the total uncertainty. The Sherpa 2.2.1 predictions for tions corresponding to Z → μμ, t t¯, diboson (V V ) and single-top (t W ) Z → μμ are shown before (top) and after (bottom) the reweighting processes. The MC distributions are cumulatively stacked. The bottom procedure (see text) Table 1 Definition of the CRs Common selections ≥ 2 jets, pT > 60 GeV, |η| < 2.5 used in the dilepton and |ηmuon | < 2.5, |ηelec | < 2.47 lepton–neutrino channel, respectively. Selections common j j ν j j to both channels are shown at the top E T > 40 GeV E T > 65 GeV Z CR 70 < m  < 110 GeV – W CR – 40 < m T < 130 GeV 0 b-jets E Tmiss > 40 GeV S>4 t t¯ CR – ≥ 2 b-jets 123 733 Page 8 of 45 Eur. Phys. J. C (2019) 79:733 105 Events / 100 GeV Events / 3 GeV ATLAS ATLAS Data Z(ee) 104 13 TeV, 36.1 fb-1 106 13 TeV, 36.1 fb-1 tt Fake eejj, Z CR eejj, Z CR VV tW 103 Data 105 tot. unc. Z(ee) 102 tt 104 Fake VV 10 tW 103 tot. unc. 1 102 10−1 10 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 1000 2000 3000 70 80 90 100 110 m min LQ [GeV] m ee [GeV] (a) (b) 105 Events / 100 GeV Events / 3 GeV ATLAS 106 ATLAS Data Z(μ μ ) 104 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 tt VV μ μ jj, Z CR 105 μ μ jj, Z CR tW tot. unc. 10 3 Data Z(μ μ ) tt 104 102 VV 10 tW 103 tot. unc. 1 102 −1 10 10 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 1000 2000 3000 70 80 90 100 110 m min LQ [GeV] m μμ [GeV] (c) (d) Fig. 3 Distributions of m min LQ and m  in the Z CR for the eejj (top) butions are cumulatively stacked. The bottom panels show the ratio of and μμjj (bottom) channels. Data are shown together with background data to expected background. The grey hatched band represents the total contributions corresponding to t t¯, diboson (V V ), Z → ee, Z → μμ, uncertainty. A reweighting as a function of m j j is applied to the Z +jets single-top (t W ) processes, and fake electrons. The background distri- simulation. The other MC predictions are not reweighted processes. After the reweighting, the overall normalisation search CRs described in the previous section. These measure- of the simulation is in very good agreement with the data in ment regions (MRs) correspond to kinematic regions where the V +jets CRs. new physics signals have already been excluded. They are chosen because several searches with related final states use similar selections for their CRs (see for example Ref. [38]). 6 Extraction of particle-level cross-sections from the measurement regions 6.1 Particle-level objects Measurements of the particle-level fiducial and differential In order to extract fiducial and differential cross-sections in cross-sections are made in several regions related to the the MRs, particle-level object selections are defined, and cho- 123 Eur. Phys. J. C (2019) 79:733 Page 9 of 45 733 Events / 100 GeV Events / 5 GeV 105 ATLAS ATLAS Data W(eν) 13 TeV, 36.1 fb-1 5 13 TeV, 36.1 fb-1 tt Fake 104 eνjj, W CR 10 eνjj, W CR Z(ee) tW, tq Data W(eν) VV W(τν) tt Fake tot. unc. 103 Z(ee) tW, tq 104 VV W(τν) 102 tot. unc. 10 103 1 102 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 500 1000 1500 2000 40 60 80 100 120 E miss T [GeV] m T [GeV] (a) (b) Events / 100 GeV Events / 5 GeV 105 ATLAS 105 ATLAS Data W(μ ν) 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 tt Z(μ μ ) 104 μ νjj, W CR μ νjj, W CR tW, tq VV Data W(μ ν) W(τν) tot. unc. 103 tt Z(μ μ ) 104 tW, tq VV 102 W(τν) tot. unc. 10 103 1 10−1 102 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 500 1000 1500 2000 40 60 80 100 120 E miss T [GeV] m T [GeV] (c) (d) Fig. 4 Distributions of E Tmiss and m T in the W CR for the eνjj (top) tively stacked. The bottom panels show the ratio of data to expected and μνjj (bottom) channels. Data are shown together with background background. The grey hatched band represents the total uncertainty. A contributions corresponding to t t¯, diboson (V V ), Z → ee, Z → μμ, reweighting as a function of m j j is applied to the W +jets and Z +jets W → μν, W → eν, W → τ ν, single-top production (t W , tq) pro- simulation. The other MC predictions are not reweighted cesses, and fake electrons. The background distributions are cumula- sen to be as close as possible to their detector-level counter- Particle-level jets are formed by clustering stable4 parti- parts described in Sect. 4. cles using the anti-kt (R = 0.4) algorithm [74], excluding Prompt dressed3 particle-level electrons and muons are muons and neutrinos. Particle-level jets are required to have used to define the fiducial region. They are required to have pT > 60 GeV and |η| < 2.5. The electrons used in the jet pT > 40 GeV and |η| < 2.5. clustering are not dressed. To match the requirements of the detector-level jet-lepton ambiguity resolution procedure, any particle-level leptons within R < 0.4 from a selected jet are removed. 3 Dressed leptons are obtained by adding the four-vectors of all photons found within a cone of size R = 0.1 around the lepton. Photons from hadron decays are excluded. 4 Particles with cτ > 10 mm are designated as stable. 123 733 Page 10 of 45 Eur. Phys. J. C (2019) 79:733 Events / 200 GeV Events / 5 GeV 106 ATLAS 106 ATLAS Data tt 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 W(eν) Fake 105 eνjj, tt CR eνjj, tt CR Z(ee) tW, tq Data tt 105 VV W(τν) 104 W(eν) Fake tot. unc. 103 Z(ee) tW, tq 104 VV W(τν) 102 tot. unc. 103 10 1 102 10−1 10 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 2000 4000 6000 8000 40 60 80 100 120 m jj [GeV] m T [GeV] (a) (b) Events / 200 GeV Events / 5 GeV ATLAS ATLAS Data tt 104 13 TeV, 36.1 fb-1 105 13 TeV, 36.1 fb-1 W(μ ν) Z(μ μ ) μ νjj, tt CR μ νjj, tt CR tW, tq VV 103 Data tt W(τν) tot. unc. W(μ ν) Z(μ μ ) 104 10 2 tW, tq VV W(τν) tot. unc. 103 10 1 102 10−1 10 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 2000 4000 6000 8000 40 60 80 100 120 m jj [GeV] m T [GeV] (c) (d) Fig. 5 Distributions of m j j and m T in the t t¯ CR for the eνjj (top) tively stacked. The bottom panels show the ratio of data to expected and μνjj (bottom) channels. Data are shown together with background background. The grey hatched band represents the total uncertainty. A contributions corresponding to t t¯ diboson (V V ), Z → ee, Z → μμ, reweighting as a function of m j j is applied to the Z +jets and W +jets W → μν, W → eν W → τ ν, single-top production (t W , tq) pro- simulations. The other MC predictions are not reweighted cesses, and fake electrons. The background distributions are cumula- 6.2 Measurement region definitions (ST ) be above 600 GeV. The requirements for events entering all MRs are summarised in Table 2. The first two MRs are identical to the eejj and μμjj Z CRs The MRs are defined both at detector level and parti- described in Table 1. The third is an eμjj region where the cle level, where the requirements are identical but use the leptons are required to have different flavours, and no require- objects passing the detector-level and particle-level selec- ment on the dilepton mass is applied. Finally, three additional tions respectively. The m j j reweighting is also applied to regions are defined as for the eejj, μμjj and eμjj MRs, and events from the simulated Drell–Yan sample at particle level, labelled as extreme with the additional requirement that the where the particle-level value of m j j is used to calculate scalar sum of the pT of the leptons and the two leading jets the weight. In each MR, the cross-section measurements 123 Eur. Phys. J. C (2019) 79:733 Page 11 of 45 733 Table 2 Requirements at particle-level for all measurement regions is accounted for by the dominant process. The variable ST refers to the (MRs) where particle-level differential cross-sections are extracted. The scalar sum of the pT of the jets and leptons purity refers to the proportion of the particle-level yield of the MR which MR Dominant process (purity) Required leptons and jets m  selection ST selection Remark eejj Z → ee (93%) = 2e; ≥ 2 jets 70 < m  < 110 GeV – Identical to Z CR μμjj Z → μμ (94%) = 2μ; ≥ 2 jets 70 < m  < 110 GeV – Identical to Z CR eμjj t t¯ → eμ (93%) = 1μ, 1e; ≥ 2 jets – – – Extreme eejj Z → ee (94%) = 2e; ≥ 2 jets 70 < m  < 110 GeV ST > 600 GeV – Extreme μμjj Z → μμ (94%) = 2μ; ≥ 2 jets 70 < m  < 110 GeV ST > 600 GeV – Extreme eμjj t t¯ → eμ (86%) = 1μ, 1e ; ≥ 2 jets – ST > 600 GeV – are made for dominant processes. The contributions from ST ; leading jet pT ; subleading jet pT ; φ and η between all other processes are subtracted from the data yield, as the leading and subleading jets; scalar sum of pT of lead- explained in Sect. 6.3. ing and subleading jets (HT ); and invariant mass of the dijet system. 6.3 Particle-level measurement method Each MR is designed such that it is dominated by a single 7 Multivariate analysis and signal regions SM process (accounting for more than 85% of the yield, see Table 2), for which the cross-section can be measured exclu- In order to discriminate between signal and background in sively. A simple procedure to extract particle-level cross- the LQ search, the TMVA [35] implementation of a BDT is sections, based on bin-by-bin correction factors, is used. In used. A number of variables are expected to provide discrim- this method, the particle-level differential cross-section for ination between signal and background. Generally, the pT of the dominant process p in bin i of the distribution of variable the leptons and jets as well as related variables such as ST will X can be expressed as: possess higher values for the signal than for the background,   in particular for high LQ mass. Since the signature arises from  q Ti p the decay of parent LQs with well-defined masses, mass- dσi p Ni − q= p Ri · Ri p = , (1) sensitive discriminating variables are further candidates for dX wi · L BDT input variables. In the dilepton channels, the lepton- jet masses m min max LQ and m LQ are reconstructed, as described where the sum runs over all sub-dominant SM processes in Sect. 5. In the lepton–neutrino channels, the mass of which contribute to the yield, Ni is the number of events one LQ (m LQ ) and the transverse mass of the second LQ p p observed in the bin, Ri (Ti ) is the yield at detector level (m TLQ ) can be reconstructed. The transverse mass is defined (particle level) for process p, wi is the width of the bin and  j L is the integrated luminosity of the data sample. The bin- as m LQ = 2 · pT · E Tmiss · (1 − cos( φ( j, E Tmiss ))), where T p p by-bin correction factors Ti /Ri may be thought of as the φ( j, E Tmiss ) is the azimuthal angle between the jet and the inverse of the reconstruction efficiency in bin i, assuming no direction of missing transverse momentum vector. The pro- bin-by-bin migrations. The bin-by-bin correction procedure cedure is analogous to that used in the dilepton channel; both doesn’t account for migration between bins due to resolution possible pairings of jet and lepton (or E Tmiss ) are tested and the effects when going from particle-level to detector-level dis- pairing that results in the smaller absolute difference between tributions. For this reason, the binning of the distributions is m LQ and m TLQ is chosen. chosen to be much broader than the experimental resolution All background processes are used in the BDT training. to minimise the migration between bins. This binning is opti- For the electron and muon channels separately, the training is mised for the measurements to ensure that at least 90% of done for each LQ mass hypothesis for which signal simula- events passing the particle-level selection remain in the same tions were produced. To ensure independence from the CRs, bin at detector level (for events passing both selections). This in the dilepton channel, only events with a dilepton invariant binning is not used for the search. mass above 130 GeV are considered in the training. Similarly, Differential cross-section measurements are made for only events with a transverse mass greater than 130 GeV are the following observables: pT of the dilepton system; φ considered in the lepton–neutrino channel. Here, to further between the leptons; minimum φ between lepton and lead- suppress the fake-electron contribution, it is required that ing jet; minimum φ between lepton and subleading jet; E Tmiss > 150 GeV and that S > 3. These requirements define 123 733 Page 12 of 45 Eur. Phys. J. C (2019) 79:733  Table 3 BDT input variables in the dilepton and lepton–neutrino chan- Z = (s + b) log(1 + s/b) − s, where s and b are the sig- nels nal and background expectations, respectively. In this opti- Channel Input variables misation, a number of further requirements are placed on the background expectations: there have to be at least two back- pT2 , j2 j j m min LQ , m  , pT , m max LQ ground events in the chosen region; the statistical uncertainty pT j2 ν j j m LQ , m TLQ , mT, E Tmiss , pT , of the number of background events estimated by the simu- lation has to be less than 20%; if less than ten background events are expected, the statistical uncertainty is required to be less than 10%. This is done to avoid selecting bins with the training regions (TRs), i.e. event samples used to train very low background expectation and thus to increase the fit the BDTs. stability. To ensure that no bias is introduced, two BDTs are used In total, there are 58 SRs in the electron and the muon for each mass point; one is trained on one half of the events channel each: two SRs (j j and ν j j) for each of the 29 mass and evaluated on the other half, and vice versa for the second hypotheses considered. The fraction of signal events that is BDT. Different methods of dividing the simulation samples left after acceptance selections and losses due to detector into training and test samples are tested and compared, giving inefficiencies, is estimated from the simulation to rise from consistent results such that no overtraining or other biases are less than 1% at low masses (around 200 GeV) to 60–70% for introduced in the process. masses around 1500 GeV. Table 3 lists the variables that were found to give optimal sensitivity, ordered by their discriminating power in the BDT. In the dilepton channel, these are both reconstructed LQ can- 8 Systematic uncertainties didate masses, m min max LQ and m LQ , the dilepton invariant mass, j2 m  , the subleading jet pT ( pT ) and the subleading lepton The systematic uncertainties for the search are divided into pT ( pT2 ). The lepton–neutrino channel also makes use of two categories: experimental uncertainties and theoretical both of the LQ mass variables, m LQ and m TLQ , and the other uncertainties in the background and the signal. Most of the variables are the transverse mass, m T , the missing transverse sources of uncertainties are common to both the search and momentum, E Tmiss , the subleading jet pT , and the charged- the measurement. In addition to the common set of uncer- lepton pT , pT . Distributions of a selection of the variables tainties, the measurement incorporates MC modelling uncer- used for training are shown in Appendix B. It was checked tainties from closure tests. The search applies uncertainties and confirmed that not only the individual input variables to normalisation factors of the major backgrounds and their but also their pairwise correlations are modelled well in the extrapolation to the SRs; these are evaluated in the CR-only simulation. profile likelihood fit in which the normalisation factors are Output distributions for the BDT discriminant in the TRs determined. In Sects. 8.1 and 8.2, the sources of experimen- are shown in Fig. 6 for a signal sample of LQ mass 1.3 TeV. tal and theoretical uncertainies are outlined, respectively. The The background distributions are shown before the fit (see impact of the major uncertainties on the search and the mea- Sect. 9). The contribution of a LQ of mass 1.3 TeV, nor- surements are described in Sects. 8.3 and 8.4, respectively. malised to the production cross-section, is also shown for the four channels. The background includes the reweighted 8.1 Experimental uncertainties predictions of Z /γ ∗ +jets and W +jets. An increased separa- tion between signal and background at higher values of the The uncertainty in the combined 2015 + 2016 integrated BDT output is seen. luminosity is 2.1%. It is derived, following a methodology The SR phase-space is a subset of that considered in the similar to that detailed in Ref. [86], and using the LUCID-2 TRs. The same object and event selections as in the TRs detector for the baseline luminosity measurements [87], from are made in the SRs, but in addition a requirement is placed calibration of the luminosity scale using x–y beam-separation on the BDT score. As stated above, each BDT is trained on scans. half of the events in the TR and evaluated on the other half, An uncertainty due to the reweighting of the vertex multi- and the obtained BDT score distribution is used to deter- plicity to describe pile-up is estimated by scaling the average mine the SR. In this way, the SR determined based on a number of interactions per bunch-crossing in data by differ- given BDT does not overlap with the TR used for that BDT. ent factors. For the dilepton and lepton–neutrino channels separately, the The major sources of uncertainties affecting the electron SRs are defined as a single bin in the output score distribu- measurement derive from the electron energy scale and reso- tion of the respective BDT. The bin range is determined for lution measurements. The analysis also considers uncertain- each mass point separately by maximising the quantity [85] ties due to the modelling of the efficiencies of the four elec- 123 Eur. Phys. J. C (2019) 79:733 Page 13 of 45 733 Events / 0.1 Events / 0.1 104 ATLAS ATLAS 13 TeV, 36.1 fb-1 10 4 13 TeV, 36.1 fb-1 103 eejj, TR eνjj, TR Data Z(ee) 10 3 Data W(eν) 10 2 tt Fake tt Fake VV tW 10 2 Z(ee) tW, tq 10 tot. unc. LQ 1.3 VV W(τν) 10 tot. unc. LQ 1.3 1 1 −1 −1 10 10 −2 10 10− 2 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 −0.5 0.0 0.5 1.0 −0.5 0.0 0.5 1.0 BDT BDT (a) (b) Events / 0.1 Events / 0.1 105 104 ATLAS ATLAS 13 TeV, 36.1 fb-1 104 13 TeV, 36.1 fb-1 10 3 μ μ jj, TR μ νjj, TR Data Z(μ μ ) 103 Data W(μ ν) 102 tt VV tt Z(μ μ ) tW tot. unc. 102 tW, tq VV 10 LQ 1.3 W(τν) tot. unc. 10 LQ 1.3 1 1 10−1 10−1 −2 10 10−2 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 −0.5 0.0 0.5 1.0 −0.5 0.0 0.5 1.0 BDT BDT (c) (d) Fig. 6 Output distributions for the BDT in the training regions (TRs) trons. The prediction for a LQ with mass 1.3 TeV is also shown. The for the four search channels: a eejj, b eνjj, c μμjj and d μνjj. Data predicted background distributions are cumulatively stacked. The bot- are shown together with pre-fit background contributions correspond- tom panels show the ratio of data to expected background. The grey ing to t t¯, diboson (V V ), Z → ee, Z → μμ, W → μν, W → eν, hatched band represents the total uncertainty W → τ ν, single-top production (t W , tq) processes, and fake elec- tron selection criteria: trigger, reconstruction, identification There are two main sources of uncertainty related to the and isolation. jet reconstruction: jet energy scale (JES) and jet energy reso- Sources of experimental uncertainties related to muon lution (JER). Another source of jet-related uncertainty corre- reconstruction and calibration include uncertainties in the sponds to corrections made for the b-tagging efficiency. The determination of the MS momentum scale, MS momen- uncertainty due to the JES is largely derived from various tum resolution, ID momentum resolution and additional in situ techniques. Four components of this uncertainty are charge-dependent corrections. Furthermore, uncertainties in considered. the determination of the four efficiency scale factors, intro- The uncertainties due to the electron, muon and jet energy duced at the end of Sect. 3.2, for trigger, identification, iso- scale and resolution are propagated to the estimation of E Tmiss . lation and track-to-vertex matching are taken into account. In addition, there is an uncertainty from the soft term, which 123 733 Page 14 of 45 Eur. Phys. J. C (2019) 79:733 has three components: one corresponding to the E Tmiss scale spond to variations in the output BDT spectra and are thus and two components for the E Tmiss resolution uncertainty. taken into account in the final limit setting calculation. The resolution uncertainty is split into components parallel The electron energy scale and reconstruction affect the and perpendicular to an axis in the transverse plane which is total background yield in the SRs for the electron channels defined along the direction of a vectorial sum of all the hard by 2–14%. Similarly uncertainties due to the muon recon- objects in the event (electrons, muons and jets). struction and calibration lead to background uncertainties of An uncertainty in the fake rate of electrons is determined between 2 and 20% for the muon channels. The impact of by varying the MC prediction that is used to remove the con- uncertainties related to the JES (JER) lead to uncertainties on tributions of prompt electrons from the fake-enriched sam- the predicted background of 5–20% (2–20%). The effect of ple by 30%. The effect is propagated to the fake background the soft-term uncertainties in the E Tmiss estimations leads to estimate. The uncertainty due to neglecting the muon fake variations of the background estimate from less than 1% to background is taken to be half the size of the electron fake 15%. Generally, the experimental uncertainties in the signal background, see Sect. 5. yields in the SRs are not larger than 2%. Uncertainties of approximately 100% for the fake electron 8.2 Theoretical uncertainties background are estimated at low values of lepton-jet mass in the TR and about 20% at masses above 1 TeV. The statistical The Sherpa 2.2.1 V +jets samples include event weights uncertainty in the electron fake rate determination is below reflecting variations of the nominal PDF set (NNPDF3.0) and 10%. As discussed earlier, the contribution from fake muons the use of two different PDF sets: MMHT2014NNLO68CL is typically half the size of the electron fake background, and [88] and CT14NNLO [89]. The NNPDF intra-PDF uncer- this is used as a (conservative) estimate of the uncertainty in tainty is estimated as the standard deviation of the set of 101 the total background due to neglecting the muon fake contri- NNPDF3.0 eigenvectors. This procedure takes into account bution. the effect of varying αS by ± 0.001 around its nominal value The final modelling uncertainties in the background yields of 0.119. The envelope of the differences between the nom- for Z +jets and W +jets are around 2–10%, with the scale inal NNPDF set and the other two PDF sets is used as an uncertainty typically being the dominant source. Signal pre- additional uncertainty. dictions have an uncertainty of a similar size. The background The samples include weights for seven sets of variation from t t¯ production is affected by a theoretical unceratinty of of the renormalisation (μr ) and factorisation (μf ) scale, i.e. up to 50%, the large value arising due to the statistical impre- varying the scales up and down by a factor of two, either cision of the MC samples. together or independently. The envelope of all these varia- tions is taken as an estimate of the scale uncertainty. 8.4 Uncertainties for the cross-section measurements A further uncertainty from the reweighting of the V +jets simulations in m j j is considered. The full difference between The uncertainties described below are those which apply to the reweighted and the unweighted distributions is taken as the particle-level cross-section measurements in the MRs, an estimate of the uncertainty induced by the reweighting. in addition to the relevant uncertainties from the previous The uncertainties in the modelling of the production of t t¯ sections. As is described in Sect. 6, the cross-section cal- are assessed from a number of alternative simulation sam- culation involves the subtraction of non-dominant processes ples. Differences between different generators and different from the observed data, and the application of bin-by-bin cor- models for fragmentation and hadronisation are considered. rection factors. The experimental uncertainties play a role in In addition, parameters affecting initial- and final-state radi- both of these steps, since they affect the detector-level yield ation are also varied. For the t t¯ sample the default PDF set of both the non-dominant processes (when subtracting from is NNPDF3.0 and the PDF uncertainty is estimated from the data) and the dominant process (when calculating the correc- PDF4LHC15 prescription [90]. tion factors). The dominant experimental uncertainties are the JES and the lepton efficiencies, which have effects of 8.3 Uncertainties for the search up to 5% and 2% on the fiducial cross-sections respectively. Theory and modelling uncertainties in the dominant process As described in Sect. 9 three CRs are fit simultaneously to affect both the particle-level and detector-level yields, and obtain normalisation factors. Sources of theoretical uncer- therefore largely cancel out when calculating the correction tainty described above are taken into account in this pro- factors T /R (see Eq. 1). cedure. Furthermore, theory uncertainties in the SRs corre- 123 Eur. Phys. J. C (2019) 79:733 Page 15 of 45 733 The theory uncertainties in the dominant processes in SRs. The uncertainty in the normalisation factors from the CR the Z +jets and t t¯ MRs therefore have only a small impact fit is introduced in the form of Gaussian nuisance parameters. (≤ 2%) on the fiducial cross-sections. The theory uncertain- The best-fit central values and constraints on other nuisance ties in the estimation of non-dominant processes generally parameters are not transferred from the CRs to the SRs. The have a minimal impact since these processes usually account optimal value and error of the signal strength and nuisance for only small fractions of the MR yield. The exception is the parameters as well as their correlations are determined simul- case of the t t¯ background to the Z MRs, which also contribute taneously when the p.d.f. is fitted to data. This conservative a small but non-negligible theory uncertainty. two-step approach is chosen for its simplicity compared with In addition, bin-by-bin correction uncertainties are derived the case of a simultaneous fit to all regions. by comparing the nominal correction factors with those For some systematic uncertainties, certain adjustments are obtained using re-weighted V +jets and alternative t t¯ sam- made before they are incorporated in the fit, as described in ples. The size of these modelling uncertainties ranges the following. Uncertainties larger than 50% are capped at between 0.5 and 4% of the fiducial cross-sections. 50%, because their large size is due to the statistical uncer- The overall uncertainty of the fiducial cross-sections, tainty of simulation samples used to estimate them. This accounting for all sources of systematic uncertainty, is applies in particular to the theoretical uncertainties in the t t¯ between 5% and 9% depending on the MR. estimation. Uncertainties with very asymmetric up and down variations are symmetrised, using the larger of the two vari- ations. In the SR fit, experimental uncertainties that before 9 Statistical procedure for the search normalisation have an effect smaller than 2% on the total background or the signal expectation are not considered in the The results of the analysis are interpreted using a profile like- fit for the background or the signal, respectively. Other nui- lihood method as implemented in HistFitter [91], in particular sance parameters are not considered for certain background using the asymptotic formulae from Ref. [85]. The signal and processes if their effect is less than 2% on that process before backgrounds are described by a binned probability density normalising the uncertainty. function (p.d.f.) built using either the three CRs described in Sect. 5, or the two SRs, one in the dilepton channel and one in the lepton–neutrino channel, as described in Sect. 7. All 10 Results CRs and SRs consist of only a single bin. The CRs are enriched in a specific background process and 10.1 Results of the cross-section measurements have a negligible signal contamination; they are thus used to normalise the predicted backgrounds to data, whereas the The measured fiducial particle-level cross-sections are shown SRs drive the signal extraction. The p.d.f. for the fit to the in Table 4 for each of the six MRs. The values in all MRs are SRs includes the signal strength, i.e. the scaling factor with found to agree with the generator predictions within uncer- respect to the predictions for the signal cross-section, as the tainties. The uncertainties in the measured cross-sections parameter of interest. Systematic uncertainties are incorpo- are in all cases dominated by experimental uncertainties rated in the p.d.f. as statistical nuisance parameters. They are (in particular the jet energy scale). The theory uncertainties introduced as shape uncertainties, i.e. they can only affect the in the generator predictions are dominated by variations in relative size of a given background in different regions while the cross-sections, arising from QCD scale variations in the preserving the overall number of expected events. This is phase-space regions of the measurements. In both the stan- done in order to remove strong correlations with the normal- dard and extreme MRs, the measured fiducial cross-sections isation factors for the main backgrounds that are extracted in the Z MRs are found to agree between electron and muon from the CRs and which are used to scale MC yields to decay modes, as expected. match data. Experimental uncertainties are treated as being Examples of the particle-level differential cross-sections fully correlated between different physics processes and fit are shown in Figs. 7 and 8, where they are compared with the regions. Theoretical or modelling uncertainties are treated as cross-sections predicted from the nominal simulation without correlated in different fit regions, but uncorrelated between the m j j reweighting. Predictions from alternative generators processes. The statistical uncertainties of the MC samples are are also shown in each case. Differential cross-sections for a included in the p.d.f. as nuisance parameters by constraint larger list of variables are included in Appendix A and in the terms described by a  distribution. The fit is performed in HEPData [92] record for this paper. two stages: first, only the CRs are included in the fit in order In the eejj and μμjj MRs (and their extreme counter- to extract the normalisation factors for the three main back- parts) the predicted differential cross-sections for variables grounds. These normalisation factors are then applied to the involving jet energies exhibit a degree of mis-modelling by main backgrounds in the second step, which is a fit to only the the Sherpa 2.2.1 simulation. This can be seen in Figs. 7 123 733 Page 16 of 45 Eur. Phys. J. C (2019) 79:733 Table 4 Measured fiducial cross-section for the dominant process in and Z → μμ samples, and Powheg+Pythia8 for the t t¯ sample. Alter- each of the MRs, with uncertainties broken down into the statistical, native generator predictions are also provided, obtained using Mad- experimental, and theoretical components. The generator predictions Graph5 FxFx and MadGraph5+Pythia8 for the Z MRs and t t¯ MRs, with their uncertainties are obtained from Sherpa 2.2.1 for the Z → ee respectively Measurement region Dominant Measured cross-section (pb) Generator prediction (pb) Alternative generator (Sherpa 2.2.1 prediction (pb) or Powheg+Pythia8) (MadGraph5) eejj Z → ee 3.28 ± 0.02 (stat.) ± 0.19 (exp.) ± 0.09 (th.) 3.24 ± 1.04 3.59 ± 0.66 μμjj Z → μμ 3.32 ± 0.02 (stat.) ± 0.21 (exp.) ± 0.09 (th.) 3.12 ± 1.01 3.63 ± 0.42 eμjj t t¯ → eμ 1.503 ± 0.010 (stat.) ± 0.130 (exp.) ± 0.002 (th.) 1.578 ± 0.085 1.702 ± 0.041 Extreme eejj Z → ee 0.483 ± 0.005 (stat.) ± 0.024 (exp.) ± 0.014 (th.) 0.511 ± 0.170 0.660 ± 0.219 Extreme μμjj Z → μμ 0.481 ± 0.004 (stat.) ± 0.022 (exp.) ± 0.014 (th.) 0.483 ± 0.161 0.484 ± 0.069 Extreme eμjj t t¯ → eμ 0.147 ± 0.003 (stat.) ± 0.011 (exp.) ± 0.001 (th.) 0.161 ± 0.032 0.146 ± 0.013 and 8, for the leading jet pT as an example. Similar mis- shows the significance [85] of the deviations, assuming Pois- modelling is seen for m j j , HT , subleading jet pT , and ST son statistics and taking only statistical uncertainties into differential measurements. All other quantities are shown account. Even without including systematic uncertainties, no to be well modelled by the nominal Sherpa 2.2.1 simu- significant excess above the SM expectation is observed in lation. In all cases, the prediction of an alternative gener- any of the SRs. The modified frequentist CLs method [94] is ator, MadGraph5_aMC@NLO (with events generated at used to set limits on the strength of the LQ signal. NLO accuracy for Z + 0, 1, 2 jets, showered using Pythia8 Each BDT uses as inputs variables that reconstruct the with the A14 tune, and for which different jet multiplici- LQ mass. Since these have a high discriminating power, the ties were merged using the FxFx prescription [93]), is also sensitivity of the SRs exhibits a strong dependence on the shown. Typically, the MadGraph5_aMC@NLO prediction LQ mass below values of m LQ around 600 GeV, where the is also discrepant with the data for the same observables, intervals between simulated mass points are greater than the but often in a direction opposite to that of the Sherpa 2.2.1 typical mass resolution for adjacent masses (the converse is prediction. This suggests that the discrepancies are caused true for masses above around 600 GeV). Therefore, in this by choices of the generator parameters, which would have regime a simple interpolation of acceptance between simu- been tuned in a different phase-space region given by pre- lated mass points does not give a reliable estimate of the limit vious Z + jets measurements. The measurements provided in the mass interval. To account for this limitation, results are by this paper will therefore help to validate new tunes of the presented separately for low and high mass regions, defined generators to ensure better agreement in this region for future as being less than and greater than 600 GeV, respectively. searches. These disagreements in modelling are covered by A coarse scan of the mass is performed in the low mass the reweighting uncertainty when evaluating limits for the region in intervals of 50 GeV. Exclusion limits that are search. obtained at these scan points are used as conservative limits For the eμjj MRs, the predictions from the nominal for the intermediate masses between these points. While for Powheg simulation are found to agree with the data for masses above ∼ 600 GeV the mass-scan intervals are smaller all observables aside from the dilepton pT , where a slight than the mass resolution at the test points, the intervals used mis-modelling is observed in the high tail, as can be seen in below ∼ 600 GeV are larger than the mass resolution. There- the examples shown in Figs. 7 and 8. The predictions of an fore, above ∼ 600 GeV a simple interpolation is used to alternative generator (MadGraph5_aMC@NLO) are also obtain the limits in the mass intervals. In the range below shown, and these do agree with the data for this observable. ∼ 600 GeV this is not appropriate and a different approach This points to the generator tuning choice as the source of is used. Since the acceptance of a given SR is highest for the disagreement. the mass point which the SR is designed for and lower at the neighbouring mass points, the limit is estimated for a given mass interval, from m 1 to m 2 , in the following way; two lim- 10.2 Results of the leptoquark search its are evaluated for m 1 and m 2 using the SR defined by m 1 (i.e. the respective BDT trained for the mass hypothesis m 1 ). The data event yields (black points) in a representative subset Similarly, two estimations are calculated for the SR defined of the SRs are shown in Fig. 9 together with the predicted by m 2 . For each SR, the weaker of the two limits is retained, background yields as evaluated with the fit. The bottom panel 123 Eur. Phys. J. C (2019) 79:733 Page 17 of 45 733 dσ / d(min Δφ(j l)) [pb/rad] dσ / d(p ) [pb/GeV] 1 ATLAS Data 4 ATLAS Data -1 -1 10 −1 13 TeV, 36.1fb Statistical error 3.5 13 TeV, 36.1fb Statistical error eejj Sherpa2.2.1 eejj Sherpa2.2.1 3 T 0 j 10− 2 MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 2.5 10− 3 2 10−4 1.5 1 10− 5 0.5 10− 6 0 1.5 1.3 Pred. / Data Pred. / Data 1 1 0.5 0.7 500 1000 1500 2000 2500 0 0.5 1 1.5 2 2.5 3 j Leading p [GeV] T min Δφ(j l)[rad] 0 dσ / d(min Δφ(j l)) [pb/rad] dσ / d(p ) [pb/GeV] 1 ATLAS Data 4 ATLAS Data -1 -1 10 −1 13 TeV, 36.1fb Statistical error 3.5 13 TeV, 36.1fb Statistical error μ μ jj Sherpa2.2.1 3 μ μ jj Sherpa2.2.1 T 0 MG5_aMC+Py8 FxFx j 10− 2 MG5_aMC+Py8 FxFx 2.5 10− 3 2 1.5 10− 4 1 10− 5 0.5 10− 6 0 1.5 1.3 Pred. / Data Pred. / Data 1 1 0.5 0.7 500 1000 1500 2000 2500 0 0.5 1 1.5 2 2.5 3 j Leading p [GeV] min Δφ(j l)[rad] T 0 dσ / d(p ) [pb/GeV] ) [pb/GeV] 1 ATLAS Data ATLAS Data −1 10 13 TeV, 36.1fb -1 Statistical error 13 TeV, 36.1fb-1 Statistical error 10− 1 eμ jj Powheg+Py8 eμ jj Powheg+Py8 10− 2 T eμ j MG5_aMC+Py8 MG5_aMC+Py8 10− 2 T dσ / d(p 10− 3 10− 3 10− 4 10− 4 10− 5 10− 5 10− 6 1.5 1.6 Pred. / Data Pred. / Data 1 1 0.5 0.4 500 1000 1500 2000 2500 0 200 400 600 800 1000 1200 1400 1600 1800 2000 j Leading p [GeV] peμ [GeV] T T Fig. 7 Examples of the measured particle-level differential cross- statistical component of the total uncertainty on the measurement which sections in the eejj, μμjj and eμjj measurement regions, exclusively is indicated by the error bar on each point. The variable min ( j0 , l) for the dominant process in each channel (Z → ee, Z → μμ and refers to the minimal difference in φ between the leading jet and a t t¯, respectively). The MC prediction for the dominant process is also prompt lepton shown, with no m j j reweighting applied. The red band represents the 123 733 Page 18 of 45 Eur. Phys. J. C (2019) 79:733 dσ / d(p ) [pb/GeV] dσ / d(min Δφ(j l)) [pb/rad] 1 10− 1 ATLAS Data ATLAS Data -1 −2 13 TeV, 36.1fb Statistical error 0.8 13 TeV, 36.1fb-1 Statistical error 10 Sherpa2.2.1 Sherpa2.2.1 Extreme eejj Extreme eejj T j 0 MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 10− 3 0.6 10− 4 0.4 10− 5 0.2 10− 6 0 1.5 1.4 Pred. / Data Pred. / Data 1 1 0.5 0.6 500 1000 1500 2000 2500 0 0.5 1 1.5 2 2.5 3 j Leading pT [GeV] min Δφ(j0 l)[rad] dσ / d(p ) [pb/GeV] dσ / d(min Δφ(j l)) [pb/rad] 10− 1 ATLAS Data 0.9 ATLAS Data -1 −2 13 TeV, 36.1fb Statistical error 0.8 13 TeV, 36.1fb-1 Statistical error 10 Extreme μ μ jj Sherpa2.2.1 0.7 Extreme μ μ jj Sherpa2.2.1 T j 0 MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx −3 0.6 10 0.5 −4 0.4 10 0.3 −5 0.2 10 0.1 10− 6 0 1.5 1.4 Pred. / Data Pred. / Data 1 1 0.5 0.6 500 1000 1500 2000 2500 0 0.5 1 1.5 2 2.5 3 j Leading p [GeV] T min Δφ(j0 l)[rad] dσ / d(p ) [pb/GeV] ) [pb/GeV] ATLAS Data ATLAS Data 10− 2 -1 -1 13 TeV, 36.1fb Statistical error 10− 2 13 TeV, 36.1fb Statistical error Extreme eμ jj Powheg+Py8 Extreme eμ jj Powheg+Py8 10− 3 T eμ j MG5_aMC+Py8 MG5_aMC+Py8 T dσ / d(p 10− 3 10− 4 10− 4 10− 5 10− 5 10− 6 1.5 1.7 Pred. / Data Pred. / Data 1 1 0.5 0.3 500 1000 1500 2000 2500 0 200 400 600 800 1000 1200 1400 1600 1800 2000 j Leading pT [GeV] peTμ [GeV] Fig. 8 Examples of the measured particle-level differential cross- which is indicated by the error bar on each point. For the leading jet pT sections in the extreme eejj, μμjj and eμjj measurement regions, exclu- measurement, the uncertainty in the second bin is smaller because the sively for the dominant process in each channel (Z → ee, Z → μμ bin-by-bin correction factor from the alternative t t¯ samples agrees more and t t¯, respectively). The MC prediction for the dominant process is closely with the nominal ones than in neighbouring bins. The variable also shown, with no m j j reweighting applied. The red band represents min ( j0 , l) refers to the minimal difference in φ between the leading the statistical component of the total uncertainty on the measurement jet and a prompt lepton 123 Eur. Phys. J. C (2019) 79:733 Page 19 of 45 733 Events Events ATLAS data ATLAS data 13 TeV, 36.1 fb-1 tt 13 TeV, 36.1 fb-1 tt 150 e channel W(eν) μ channel W(μν) 100 tW, tq tW, tq Z(ee) W(τν) 100 W(τν) Z(μμ) VV VV Fake tot. unc. tot. unc. 50 50 0 0 2.0 2.0 Significance Significance 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 −0.5 −0.5 −1.0 −1.0 −1.5 −1.5 −2.0 −2.0 lν ll lν ll lν ll lν ll lν ll lν ll lν ll lν ll lν ll lν ll mLQ [GeV] 375 600 900 1200 1500 mLQ [GeV] 375 600 900 1200 1500 (a) (b) Fig. 9 Data and background yields in the SR for different values of tributions correspond to t t¯, W +jets, diboson (V V ), Z +jets, single-top LQ mass for a first- and b second-generation LQs. Each bin in these (W t, W q) processes, and fake electrons. The background distributions plots corresponds to one SR, the bin label indicating the channel, i.e. are cumulatively stacked. The grey band indicates the total uncertainty dilepton or lepton–neutrino. Two consecutive bins show the two SRs in the background estimate after the fit. The bottom panel shows the for a given mass hypothesis, that is indicated at the bottom of the plots. significance of the deviations, taking only statistical uncertainties into Mass points from 375 GeV to 1.5 TeV are shown. The background con- account leaving two limits to consider, one from each SR. Of these 11 Summary and conclusions two remaining results from the two SRs, the stronger limit is used. Searches for pair production of first- and second-generation √ The 95% CL upper limits on the cross-section for scalar- scalar leptoquarks in pp collisions at s = 13 TeV have LQ pair production, normalised to the predicted cross- been made using the ATLAS detector at the LHC. The section, are presented as a function of m LQ in Fig. 10 for searches exploit a data set corresponding to 36.1 fb−1 of inte- first- and second-generation LQs for an assumed value of grated luminosity and probe the lepton–quark and lepton– β = 0.5. Expected limits with their one and two standard- neutrino LQ decay channels. No significant excess above the deviation bands are also shown. The observed limits are con- SM background expectation is observed in any channel and sistent with the expected limits for all mass points for both exclusion limits have been evaluated. The results presented channels. Lower mass limits of about 1.25 TeV are obtained, here significantly extend the sensitivity in mass compared to representing an increase of around 400 GeV compared with previous ATLAS results, and yield an exclusion very simi- earlier work [21]. lar to that found by the CMS experiment using a dataset of a Exclusion contours in the β–m LQ plane are shown in similar size [27,28]. Within the minimal Buchmüller–Rückl– Fig. 11 for different values of β for the first two LQ gen- Wyler model and assuming a branching ratio for the decay erations. The sensitivity in β is greatest for the lowest mass into a charged lepton and a quark of 50%, leptoquarks with region considered (∼ 200 GeV) for which the search is sensi- masses up to 1.29 TeV are excluded at 95% CL for first gener- tive to β values around 10−2 . For β = 1, a mass of 1400 GeV ation leptoquarks, and up to 1.23 TeV for second generation (1560 GeV) can be excluded for first-generation (second- leptoquarks. generation) LQs. Limit values are also given for first- and In addition to the search, measurements have been made second-generation LQs in Table 5. The considerably stronger of particle-level fiducial and differential cross-sections for observed limit for the second generation is due to a downward six measurement regions related to the control regions used fluctuation in the data in the relevant SRs. for the search. Two measurement regions are identical to the Z control regions used in the search, and their fiducial cross- 123 733 Page 20 of 45 Eur. Phys. J. C (2019) 79:733 10 10 95% CL limit on σ/ σth 95% CL limit on σ/ σth ATLAS Expected ATLAS 13 TeV, 36.1 fb-1 Expected ± 1σ 13 TeV, 36.1 fb-1 e channel Expected ± 2σ e channel Observed 1 1 10−1 10−1 Observed Expected Expected ± 1σ Expected ± 2σ 10−2 10−2 200 300 400 500 600 600 800 1000 1200 1400 m LQ [GeV] m LQ [GeV] (a) (b) 10 10 95% CL limit on σ/ σth 95% CL limit on σ/ σth ATLAS Expected ATLAS 13 TeV, 36.1 fb-1 Expected ± 1σ 13 TeV, 36.1 fb-1 μ channel Expected ± 2σ μ channel Observed 1 1 10−1 10−1 Observed Expected Expected ± 1σ Expected ± 2σ 10−2 10−2 200 300 400 500 600 600 800 1000 1200 1400 m LQ [GeV] m LQ [GeV] (c) (d) Fig. 10 Upper limits (observed and expected) on the pair-production high (right) mass regions, which correspond to masses less than and cross-section for (top) first- and (bottom) second-generation LQs nor- greater than 600 GeV, respectively. The ± 1σ (±2σ ) uncertainty bands malised to the predicted cross-section (σ/σth ), as a function of LQ mass on the expected limit represent all sources of systematic and statistical for an assumed value of β = 0.5. Limits are presented for low (left) and uncertainty 123 Eur. Phys. J. C (2019) 79:733 Page 21 of 45 733 1 1 β β ATLAS Expected ATLAS 13 TeV, 36.1 fb-1 Expected ± 1σ 13 TeV, 36.1 fb-1 e channel Expected ± 2σ e channel Observed 10−1 10−1 10−2 10−2 Expected Expected ± 1σ Expected ± 2σ Observed 200 300 400 500 600 600 800 1000 1200 1400 1600 m LQ [GeV] m LQ [GeV] (a) (b) 1 1 β ATLAS Expected β ATLAS 13 TeV, 36.1 fb-1 Expected ± 1σ 13 TeV, 36.1 fb-1 μ channel Expected ± 2σ μ channel Observed 10−1 10−1 10−2 10−2 Expected Expected ± 1σ Expected ± 2σ Observed 200 300 400 500 600 600 800 1000 1200 1400 1600 m LQ [GeV] m LQ [GeV] (c) (d) Fig. 11 Upper limits (observed and expected) on the branching ratio greater than 600 GeV, respectively. The ± 1σ (±2σ ) uncertainty bands for (top) first- and (bottom) second-generation LQs into a lepton and on the expected limit represent all sources of systematic and statistical quark as a function of LQ mass. Limits are presented for low (left) and uncertainty high (right) mass regions, which correspond to masses less than and sections are measured to be 3.28±0.22 pb and 3.32±0.23 pb regions may be useful for generator tuning since they repre- for the Z → ee and Z → μμ channels respectively. The sent regions which are utilised in searches but where mea- measurements agree with the cross-sections predicted by surements are rarely made. Sherpa 2.2.1, which are 3.24 ± 1.02 pb and 3.12 ± 1.01 pb In addition to the inclusive cross-section measurements, respectively. A third measurement region is dominated by differential measurements are made for eleven variables: the t t¯ → eμ, where the fiducial cross-section is measured to be transverse momenta of the leading and subleading jets, the 1.50 ± 0.13 pb, compared to 1.60 ± 0.38 pb predicted by minimum angles between the leading and subleading jets Powheg+Pythia8. Measurements are also made in three with a lepton, the dilepton transverse momentum, the open- other regions where the scalar sum of the pT of the jets ing angle between the leptons, the scalar sum of the jet trans- and leptons is above 600 GeV. These so-called “extreme” verse momenta, the scalar sum of the jet and lepton transverse 123 733 Page 22 of 45 Eur. Phys. J. C (2019) 79:733 Table 5 Expected and observed 95% CL lower limits on first- and Marie Skłodowska-Curie Actions, European Union; Investissements d’ second-generation LQ masses in units of GeV for different values of Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Ger- the branching ratio β into a charged lepton and quark many; Herakleitos, Thales and Aristeia programmes co-financed by EU- ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Expected Observed Expected Observed Programme Generalitat de Catalunya, Spain; The Royal Society and (m LQ1 ) (m LQ1 ) (m LQ2 ) (m LQ2 ) Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from 1.0 1400 1400 1400 1560 CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Den- 0.5 1280 1290 1200 1230 mark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), 0.1 1020 1010 960 960 INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Tai- wan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [95]. momenta, and the opening angles in η and φ between the two leading jets. In the Z measurement regions, the differential Data Availability Statement This manuscript has no associated data or the data will not be deposited. [Authors’ comment: “All ATLAS sci- cross-sections for variables involving leptons are typically entific output is published in journals, and preliminary results are made found to be well-modelled by the nominal generators and available in Conference Notes. All are openly available, without restric- the alternative generators shown typically give reasonable but tion on use by external parties beyond copyright law and the standard weaker agreement. The measurements involving jet energies conditions agreed by CERN. Data associated with journal publications are also made available: tables and data from plots (e.g. cross section and angles are found to exhibit a degree of mis-modelling values, likelihood profiles, selection efficiencies, cross section limits, by both the nominal and alternative simulations. The nomi- ...) are stored in appropriate repositories such as HEPDATA (http:// nal and alternative generator predictions are often discrepant hepdata.cedar.ac.uk/). ATLAS also strives to make additional material with respect to the data in different directions, indicating related to the paper available that allows a reinterpretation of the data in the context of new theoretical models. For example, an extended that these differences are symptoms of different choices encapsulation of the analysis is often provided for measurements in the of the generator parameters, which would have been tuned framework of RIVET (http://rivet.hepforge.org/).” This information is in less extreme regions of phase-space given by previous taken from the ATLAS Data Access Policy, which is a public docu- Z + jets measurements. Measurements in the regions of phase ment that can be downloaded from http://opendata.cern.ch/record/413 [opendata.cern.ch].] space used as control regions by this and related searches have not been made directly in the past. Therefore, the mea- Open Access This article is distributed under the terms of the Creative surements provided by this paper can be used to help validate Commons Attribution 4.0 International License (http://creativecomm new generator versions and parameter choices in this region ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit of phase space, and thus help improve the modelling of the to the original author(s) and the source, provide a link to the Creative background for future searches. Commons license, and indicate if changes were made. Funded by SCOAP3 . Acknowledgements We thank CERN for the very successful oper- ation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowl- edge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Appendix Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; A MR differential cross-section measurements CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIEN- CIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Repub- lic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, The measured differential cross-sections in the MRs are France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, shown in Fig. 12 for the eejj channel, in Fig. 13 for the μμjj Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, channel and in Fig. 14 for the eμjj channel. Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, The measured differential cross-sections in the MRs Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portu- gal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federa- where an additional selection of ST > 600 GeV has been tion; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slove- applied are shown in Fig. 15 for the eejj channel, in Fig. 16 nia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg for the μμjj channel, and in Fig. 17 for the eμjj channel. 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 123 Eur. Phys. J. C (2019) 79:733 Page 23 of 45 733 dσ / d(min Δφ(j l)) [pb/rad] dσ / d(p ) [pb/GeV] dσ / d(H ) [pb/GeV] 10 3 ATLAS Data ATLAS Data ATLAS Data -1 1 1 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb-1 Statistical error 13 TeV, 36.1fb-1 Statistical error 2.5 Sherpa2.2.1 10− 1 eejj 10 −1 eejj Sherpa2.2.1 eejj Sherpa2.2.1 1 T MG5_aMC+Py8 FxFx j T MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 10− 2 2 10− 2 10− 3 10− 3 1.5 10− 4 10− 4 10− 5 1 10− 5 10− 6 0.5 10− 6 10− 7 1.2 1.5 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.8 0.5 −10 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 500 1000 1500 2000 2500 3000 3500 4000 min Δφ(j l)[rad] j Subleading pT [GeV] HT [GeV] 1 ) [pb/GeV] dσ / d(Δη(jj)) [pb] dσ / d(Δφ(jj)) [pb/rad] 10 4.5 ATLAS Data 3 ATLAS Data 4 ATLAS Data -1 13 TeV, 36.1fb-1 -1 Statistical error 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb Statistical error 1 Sherpa2.2.1 Sherpa2.2.1 3.5 Sherpa2.2.1 eejj 2.5 eejj eejj μμ MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 3 MG5_aMC+Py8 FxFx T dσ / d(p 10− 1 2 2.5 1.5 2 10− 2 1.5 1 10− 3 1 0.5 0.5 10− 4 0 0 1.2 1.4 1.3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.8 0.6 0.7 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 μμ pT [GeV] Δη(jj) Δφ(jj)[rad] dσ / d(Δφ(ll)) [pb/rad] dσ / d(M ) [pb/GeV] dσ / d(S ) [pb/GeV] 3.5 ATLAS Data ATLAS Data 1 ATLAS Data 3 13 TeV, 36.1fb-1 Statistical error 1 13 TeV, 36.1fb -1 Statistical error 13 TeV, 36.1fb -1 Statistical error 10− 1 eejj Sherpa2.2.1 eejj Sherpa2.2.1 eejj Sherpa2.2.1 2.5 10− 1 10− 2 jj T MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 2 10− 3 10− 2 1.5 10− 4 1 10− 3 10− 5 0.5 10− 6 10− 4 0 10− 7 1.4 1.3 2.2 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.6 0.7 −0.2 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 Δφ(ll)[rad] Mjj [GeV] ST [GeV] Fig. 12 The measured generator-level differential cross-sections in the min ( j1 , l) refers to the minimal difference in φ between the sub- eejj channel, exclusively for the Z → ee process. The MC prediction for leading jet and a prompt lepton. The variable HT refers to the scalar the dominant process is also shown, with no m j j reweighting applied. sum of the jet pT values. The variables η( j j), φ( j j) and φ(ll) The red band represents the statistical component of the total uncer- refer to the difference in η and φ between the two leading jets or leptons tainty which is indicated by the error bar on each point. The variable 123 733 Page 24 of 45 Eur. Phys. J. C (2019) 79:733 dσ / d(min Δφ(j l)) [pb/rad] dσ / d(p ) [pb/GeV] dσ / d(H ) [pb/GeV] 10 3 ATLAS Data ATLAS Data ATLAS Data 1 13 TeV, 36.1fb-1 Statistical error 1 13 TeV, 36.1fb -1 Statistical error 13 TeV, 36.1fb -1 Statistical error μ μ jj Sherpa2.2.1 10− 1 2.5 10− 1 μ μ jj Sherpa2.2.1 μ μ jj Sherpa2.2.1 1 T MG5_aMC+Py8 FxFx j T MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 10− 2 2 10− 2 10− 3 10− 3 1.5 10− 4 10− 4 10− 5 1 10− 5 10− 6 0.5 10− 6 10− 7 1.2 1.6 2.8 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.8 0.4 −0.8 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 3000 3500 4000 min Δφ(j1 l)[rad] j Subleading pT [GeV] HT [GeV] ) [pb/GeV] dσ / d(Δη(jj)) [pb] dσ / d(Δφ(jj)) [pb/rad] 10 4.5 ATLAS Data 3 ATLAS Data ATLAS Data 4 13 TeV, 36.1fb -1 Statistical error 13 TeV, 36.1fb-1 Statistical error 13 TeV, 36.1fb-1 Statistical error 1 μ μ jj Sherpa2.2.1 2.5 μ μ jj Sherpa2.2.1 3.5 μ μ jj Sherpa2.2.1 μμ MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx T 3 dσ / d(p 10− 1 2 2.5 1.5 2 10− 2 1 1.5 10− 3 1 0.5 0.5 −4 10 0 0 1.2 1.3 1.3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.8 0.7 0.7 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 pμμ [GeV] Δη(jj) Δφ(jj)[rad] T dσ / d(Δφ(ll)) [pb/rad] dσ / d(M ) [pb/GeV] dσ / d(S ) [pb/GeV] 3.5 ATLAS Data ATLAS Data 1 ATLAS Data 3 13 TeV, 36.1fb-1 Statistical error 1 13 TeV, 36.1fb-1 Statistical error 13 TeV, 36.1fb-1 Statistical error 10− 1 μ μ jj Sherpa2.2.1 μ μ jj Sherpa2.2.1 μ μ jj Sherpa2.2.1 2.5 10− 1 10− 2 jj T MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 2 10− 3 10− 2 1.5 10− 4 1 10− 3 10− 5 0.5 10− 6 10− 4 0 10− 7 1.3 1.3 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.7 0 0.5 1 1.5 2 2.5 3 0.7 0 500 1000 1500 2000 2500 3000 3500 4000 −10 500 1000 1500 2000 2500 3000 3500 4000 Δφ(ll)[rad] Mjj [GeV] ST [GeV] Fig. 13 The measured generator-level differential cross-sections in the able min ( j1 , l) refers to the minimal difference in φ between the μμjj channel, exclusively for the Z → μμ process. The MC predic- subleading jet and a prompt lepton. The variable HT refers to the scalar tion for the dominant process is also shown, with no m j j reweighting sum of the jet pT values. The variables η( j j), φ( j j) and φ(ll) applied. The red band represents the statistical component of the total refer to the difference in η and φ between the two leading jets or leptons. uncertainty which is indicated by the error bar on each point. The vari- The variable ST refers to the scalar sum of the pT of the jets and leptons 123 Eur. Phys. J. C (2019) 79:733 Page 25 of 45 733 dσ / d(min Δφ(j l)) [pb/rad] dσ / d(min Δφ(j l)) [pb/rad] dσ / d(p ) [pb/GeV] 1.6 ATLAS Data 1.8 ATLAS Data 1 ATLAS Data 1.4 -1 -1 -1 13 TeV, 36.1fb Statistical error 1.6 13 TeV, 36.1fb Statistical error 10− 1 13 TeV, 36.1fb Statistical error 1.2 eμ jj Powheg+Py8 eμ jj Powheg+Py8 eμ jj Powheg+Py8 1.4 10− 2 T 1 0 j MG5_aMC+Py8 MG5_aMC+Py8 MG5_aMC+Py8 1 1.2 10− 3 0.8 1 10− 4 0.6 0.8 10− 5 0.4 0.6 10− 6 0.2 0.4 10− 7 0 0.2 10− 8 1.9 1.3 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.1 0.7 −1 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 j min Δφ(j0 l)[rad] min Δφ(j1 l)[rad] Subleading pT [GeV] dσ / d(H ) [pb/GeV] dσ / d(Δη(jj)) [pb] dσ / d(Δφ(jj)) [pb/rad] 10 1.6 2.2 1 ATLAS Data ATLAS Data 2 ATLAS Data -1 1.4 -1 -1 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb Statistical error 1.8 13 TeV, 36.1fb Statistical error 10− 1 eμ jj Powheg+Py8 1.2 eμ jj Powheg+Py8 1.6 eμ jj Powheg+Py8 10− 2 T MG5_aMC+Py8 MG5_aMC+Py8 MG5_aMC+Py8 1 1.4 10− 3 1.2 0.8 10− 4 1 0.6 0.8 10− 5 0.4 0.6 10− 6 0.4 −7 0.2 10 0.2 10− 8 0 0 2 1.3 1.3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0 0.7 0.7 0 200 400 600 800 1000 1200 1400 1600 1800 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 HT [GeV] Δη(jj) Δφ(jj)[rad] 102 dσ / d(Δφ(ll)) [pb/rad] dσ / d(M ) [pb/GeV] dσ / d(S ) [pb/GeV] 1.6 ATLAS Data ATLAS Data 10 ATLAS Data 10− 1 13 TeV, 36.1fb -1 Statistical error 13 TeV, 36.1fb -1 Statistical error 1 13 TeV, 36.1fb -1 Statistical error 1.4 10− 1 eμ jj Powheg+Py8 eμ jj Powheg+Py8 eμ jj Powheg+Py8 1.2 10− 2 10− 2 jj T MG5_aMC+Py8 MG5_aMC+Py8 MG5_aMC+Py8 10− 3 1 10− 4 0.8 10− 3 10− 5 10− 6 0.6 10− 7 0.4 10− 4 10− 8 10− 9 0.2 10 − 10 0 10− 5 10 − 11 1.3 1.2 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.7 0.8 −1 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 Δφ(ll)[rad] mjj [GeV] ST [GeV] Fig. 14 The measured generator-level differential cross-sections in the and min ( j1 , l) refers to the minimal difference in φ between a eμjj channel, defined as for the eejj and μμjj MRs, but requiring the prompt lepton and the leading or subleading jet respectively. The vari- two leptons to have different flavours, and with no requirement on the able HT refers to the scalar sum of the jet pT values. The variables dilepton mass. The measurements are made exclusively for the t t¯ pro- η( j j), φ( j j) and φ(ll) refer to the difference in η and φ between cess. The MC prediction for the dominant process is also shown. The red the two leading jets or leptons. The variable ST refers to the scalar sum band represents the statistical component of the total uncertainty which of the pT of the jets and leptons is indicated by the error bar on each point. The variables min ( j0 , l) 123 733 Page 26 of 45 Eur. Phys. J. C (2019) 79:733 dσ / d(min Δφ(j l)) [pb/rad] dσ / d(p ) [pb/GeV] dσ / d(H ) [pb/GeV] 1 10− 1 0.45 ATLAS Data ATLAS Data ATLAS Data 0.4 13 TeV, 36.1fb-1 Statistical error 10 −1 13 TeV, 36.1fb -1 Statistical error 10− 2 13 TeV, 36.1fb -1 Statistical error Extreme eejj Sherpa2.2.1 Sherpa2.2.1 Sherpa2.2.1 Extreme eejj Extreme eejj 0.35 1 10− 2 T MG5_aMC+Py8 FxFx 10− 3 j T MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 0.3 −3 10 10− 4 0.25 0.2 10− 4 10− 5 0.15 10− 5 10− 6 0.1 10− 6 10− 7 1.2 1.5 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.8 0.5 −10 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 500 1000 1500 2000 2500 3000 3500 4000 min Δφ(j1 l)[rad] j Subleading p [GeV] HT [GeV] T dσ / d(pee ) [pb/GeV] dσ / d(Δη(jj)) [pb] dσ / d(Δφ(jj)) [pb/rad] 0.5 10− 1 0.7 ATLAS Data ATLAS Data ATLAS Data -1 -1 13 TeV, 36.1fb Statistical error 0.4 13 TeV, 36.1fb Statistical error 0.6 13 TeV, 36.1fb-1 Statistical error Extreme eejj Sherpa2.2.1 Extreme eejj Sherpa2.2.1 Extreme eejj Sherpa2.2.1 0.5 10− 2 MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx T 0.3 0.4 0.2 0.3 10− 3 0.2 0.1 0.1 10− 4 0 0 2.2 1.4 1.4 Pred. / Data Pred. / Data Pred. / Data 1 1 1 −0.2 0.6 0.6 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 pee [GeV] Δη(jj) Δφ(jj)[rad] T dσ / d(Δφ(ll)) [pb/rad] dσ / d(S ) [pb/GeV] dσ / d(M ) [pb/GeV] 10− 1 0.9 ATLAS Data ATLAS Data ATLAS Data -1 -1 -1 0.8 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb Statistical error 10− 2 13 TeV, 36.1fb Statistical error Extreme eejj Sherpa2.2.1 10− 2 Extreme eejj Sherpa2.2.1 Extreme eejj Sherpa2.2.1 0.7 10− 3 T jj MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 0.6 0.5 10− 4 10− 3 0.4 0.3 10− 5 0.2 10 −4 10− 6 0.1 0 10− 7 3 1.3 2.2 Pred. / Data Pred. / Data Pred. / Data 1 1 1 −10 0.7 −0.2 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 Δφ(ll)[rad] mjj [GeV] ST [GeV] Fig. 15 The measured generator-level differential cross-sections in the min ( j1 , l) refers to the minimal difference in φ between the sub- eejj channel, where an additional selection of ST > 600 GeV has been leading jet and a prompt lepton. The variable HT refers to the scalar sum applied, exclusively for the Z → ee process. The MC prediction for of the jet pT values. The variables η( j j), φ( j j) and φ(ll) refer to the dominant process is also shown, with no m j j reweighting applied. the difference in η and φ between the two leading jets or leptons. The The red band represents the statistical component of the total uncer- variable ST refers to the scalar sum of the pT of the jets and leptons tainty which is indicated by the error bar on each point. The variable 123 Eur. Phys. J. C (2019) 79:733 Page 27 of 45 733 dσ / d(p ) [pb/GeV] dσ / d(H ) [pb/GeV] dσ / d(min Δφ(j l)) [pb/rad] 1 10− 1 0.45 ATLAS Data ATLAS Data ATLAS Data -1 −1 -1 -1 0.4 13 TeV, 36.1fb Statistical error 10 13 TeV, 36.1fb Statistical error 10− 2 13 TeV, 36.1fb Statistical error 0.35 Extreme μ μ jj Sherpa2.2.1 Extreme μ μ jj Sherpa2.2.1 Extreme μ μ jj Sherpa2.2.1 10− 2 T 10− 3 1 j T MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 0.3 10− 3 10− 4 0.25 −4 10 0.2 10− 5 0.15 10− 5 10− 6 0.1 −6 10 10− 7 1.2 1.5 2.8 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.8 0.5 −0.8 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 3000 3500 4000 j min Δφ(j l)[rad] Subleading pT [GeV] HT [GeV] 1 ) [pb/GeV] dσ / d(Δη(jj)) [pb] dσ / d(Δφ(jj)) [pb/rad] 0.5 0.8 10− 1 ATLAS Data ATLAS Data ATLAS Data -1 -1 0.7 13 TeV, 36.1fb Statistical error 0.4 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb-1 Statistical error Extreme μ μ jj Sherpa2.2.1 Extreme μ μ jj Sherpa2.2.1 0.6 Extreme μ μ jj Sherpa2.2.1 10− 2 μμ MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx T 0.5 dσ / d(p 0.3 0.4 0.2 10− 3 0.3 0.2 0.1 0.1 10− 4 0 0 1.3 1.4 1.5 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.7 0.6 0.5 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 pμμ [GeV] Δη(jj) Δφ(jj)[rad] T dσ / d(Δφ(ll)) [pb/rad] dσ / d(M ) [pb/GeV] dσ / d(S ) [pb/GeV] 0.9 10− 1 ATLAS Data ATLAS Data ATLAS Data 0.8 -1 -1 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb Statistical error 10− 2 13 TeV, 36.1fb-1 Statistical error −2 0.7 Extreme μ μ jj Sherpa2.2.1 10 Extreme μ μ jj Sherpa2.2.1 Extreme μ μ jj Sherpa2.2.1 10− 3 jj T 0.6 MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx MG5_aMC+Py8 FxFx 0.5 10− 4 0.4 10− 3 0.3 10− 5 0.2 10− 4 10− 6 0.1 0 10− 7 1.8 1.3 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.2 0 0.5 1 1.5 2 2.5 3 0.7 0 500 1000 1500 2000 2500 3000 3500 4000 −10 500 1000 1500 2000 2500 3000 3500 4000 Δφ(ll)[rad] mjj [GeV] ST [GeV] Fig. 16 The measured generator-level differential cross-sections in the point. The variable min ( j1 , l) refers to the minimal difference in φ μμjj channel, where an additional selection of ST > 600 GeV has been between the subleading jet and a prompt lepton. The variable HT refers applied. The measurements are made exclusively for the Z → μμ pro- to the scalar sum of the jet pT values. The variables η( j j), φ( j j) cess. The MC prediction for the dominant process is also shown, with no and φ(ll) refer to the difference in η and φ between the two leading m j j reweighting applied. The red band represents the statistical compo- jets or leptons. The variable ST refers to the scalar sum of the pT of the nent of the total uncertainty which is indicated by the error bar on each jets and leptons 123 733 Page 28 of 45 Eur. Phys. J. C (2019) 79:733 dσ / d(min Δφ(j l)) [pb/rad] dσ / d(min Δφ(j l)) [pb/rad] dσ / d(p ) [pb/GeV] 0.12 ATLAS Data ATLAS Data 10− 1 ATLAS Data -1 0.25 -1 -1 0.1 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb Statistical error 13 TeV, 36.1fb Statistical error 10− 2 Extreme eμ jj Powheg+Py8 0.2 Extreme eμ jj Powheg+Py8 Extreme eμ jj Powheg+Py8 T 1 10− 3 0 j 0.08 MG5_aMC+Py8 MG5_aMC+Py8 MG5_aMC+Py8 0.15 10− 4 0.06 10− 5 0.04 0.1 10− 6 0.02 0.05 10− 7 0 0 10− 8 2.1 1.3 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 −0.1 0.7 −1 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 j min Δφ(j0 l)[rad] min Δφ(j1 l)[rad] Subleading pT [GeV] dσ / d(H ) [pb/GeV] dσ / d(Δη(jj)) [pb] dσ / d(Δφ(jj)) [pb/rad] 10 0.3 1 ATLAS Data 0.16 ATLAS Data ATLAS Data -1 -1 13 TeV, 36.1fb Statistical error 0.14 13 TeV, 36.1fb Statistical error 0.25 13 TeV, 36.1fb-1 Statistical error 10− 1 Extreme eμ jj Powheg+Py8 0.12 Extreme eμ jj Powheg+Py8 Extreme eμ jj Powheg+Py8 10− 2 T MG5_aMC+Py8 MG5_aMC+Py8 0.2 MG5_aMC+Py8 10− 3 0.1 0.08 0.15 10− 4 10− 5 0.06 0.1 10− 6 0.04 0.05 10− 7 0.02 10− 8 0 0 2 1.4 1.5 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0 0.6 0.5 0 200 400 600 800 1000 1200 1400 1600 1800 0 2 4 6 8 10 0 0.5 1 1.5 2 2.5 3 HT [GeV] Δη(jj) Δφ(jj)[rad] 102 dσ / d(Δφ(ll)) [pb/rad] dσ / d(M ) [pb/GeV] dσ / d(S ) [pb/GeV] 0.16 ATLAS Data 10− 2 ATLAS Data 10 ATLAS Data 13 TeV, 36.1fb -1 Statistical error 13 TeV, 36.1fb -1 Statistical error 1 13 TeV, 36.1fb -1 Statistical error 0.14 10− 1 Extreme eμ jj Powheg+Py8 Extreme eμ jj Powheg+Py8 10−2 Extreme eμ jj Powheg+Py8 0.12 jj MG5_aMC+Py8 MG5_aMC+Py8 T MG5_aMC+Py8 10− 3 0.1 10− 3 10− 4 0.08 10− 5 10− 6 0.06 10− 4 10− 7 0.04 10− 8 10− 9 0.02 10 − 10 0 10− 5 10− 11 1.8 1.3 3 Pred. / Data Pred. / Data Pred. / Data 1 1 1 0.2 0.7 −1 0 0.5 1 1.5 2 2.5 3 0 500 1000 1500 2000 2500 3000 3500 4000 1000 1500 2000 2500 3000 3500 4000 Δφ(ll)[rad] mjj [GeV] ST [GeV] Fig. 17 The measured generator-level differential cross-sections in the indicated by the error bar on each point. The variables min ( j0 , l) eμjj channel, defined as for the eejj and μμjj MRs, but requiring the and min ( j1 , l) refers to the minimal difference in φ between a two leptons to have different flavours, and with no requirement on the prompt lepton and the leading or subleading jet respectively. The vari- dilepton mass, where an additional selection of ST > 600 GeV has been able HT refers to the scalar sum of the jet pT values. The variables applied. The measurements are made exclusively for the t t¯ process. The η( j j), φ( j j) and φ(ll) refer to the difference in η and φ between MC prediction for the dominant process is also shown. The red band the two leading jets or leptons. The variable ST refers to the scalar sum represents the statistical component of the total uncertainty which is of the pT of the jets and leptons 123 Eur. Phys. J. C (2019) 79:733 Page 29 of 45 733 B Training region distributions criminating variables is shown (m min T LQ for the dilepton, m LQ for the lepton-neutrino channel). In addition, the E Tmiss dis- Figures 18 and 19 show kinematic distributions for the TRs tribution is shown for the lepton-neutrino channel, as this is a for the dilepton and lepton–neutrino channels, respectively. characteristic variable for this channel newly included in this These regions corresponds to the SRs albeit with the BDT search. For symmetry, the subleading lepton pT is shown in selection removed. For each channel, one of the most dis- the dilepton channel. Events / 100 GeV Events / 100 GeV 104 ATLAS ATLAS 104 10 3 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 eejj, TR eejj, TR Data Z(ee) 103 Data Z(ee) 102 tt Fake tt Fake 10 VV tW 102 VV tW tot. unc. LQ 1.2 tot. unc. LQ 1.2 1 10 −1 10 1 10−2 10−1 10−3 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 1000 2000 3000 0 500 1000 1500 2000 m min LQ [GeV] p e, T sublead [GeV] (a) (b) Events / 100 GeV Events / 100 GeV 104 ATLAS ATLAS 104 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 103 μ μ jj, TR μ μ jj, TR Data Z(μ μ ) 103 Data 102 tt VV Z(μ μ ) 10 tW tot. unc. 102 tt LQ 1.2 VV 10 tW 1 tot. unc. −1 LQ 1.2 10 1 −2 10 10−1 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 1000 2000 3000 0 500 1000 1500 2000 μ, sublead m min LQ [GeV] p [GeV] T (c) (d) Fig. 18 Distributions of m min e2 LQ (left) and pT (right) in the training processes, and fake electrons. The background distributions are cumu- regions (TRs) for the BDT for the eejj (top) and μμjj (bottom) channels. latively stacked. The bottom panels show the ratio of data to expected Data are shown together with background contributions corresponding background. The grey hatched band represents the total uncertainty. to t t¯, diboson (V V ), Z → τ τ , Z → ee, Z → μμ, single-top (t W ) Predictions for a LQ with mass of 1.2 TeV are also shown 123 733 Page 30 of 45 Eur. Phys. J. C (2019) 79:733 Events / 100 GeV Events / 100 GeV ATLAS 104 ATLAS 104 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 eνjj, TR 10 3 eνjj, TR 103 Data W(eν) Data W(eν) tt Fake 102 tt Fake 102 Z(ee) tW, tq Z(ee) tW, tq VV W(τν) 10 VV W(τν) 10 tot. unc. LQ 1.2 tot. unc. LQ 1.2 1 1 −1 10 10−1 −2 10 10−2 Data / Pred. Data / Pred. 1.5 1.5 1.0 1.0 0.5 0.5 0 1000 2000 3000 500 1000 1500 2000 m TLQ [GeV] E miss T [GeV] (a) (b) 105 Events / 100 GeV Events / 100 GeV ATLAS 105 ATLAS 104 13 TeV, 36.1 fb-1 13 TeV, 36.1 fb-1 μ νjj, TR 10 4 μ νjj, TR 103 Data W(μ ν) Data W(μ ν) tt Z(μ μ ) 103 tt Z(μ μ ) 102 tW, tq VV tW, tq VV 102 W(τν) tot. unc. W(τν) tot. unc. 10 LQ 1.2 LQ 1.2 10 1 1 10−1 10 −1 10−2 10−2 Data / Pred. 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Zwalinski35 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany, NY, USA 3 Department of Physics, University of Alberta, Edmonton, AB, Canada 4 (a) Department of Physics, Ankara University, Ankara, Turkey; (b) Istanbul Aydin University, Istanbul, Turkey; (c) Division of Physics, 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, USA 7 Department of Physics, University of Arizona, Tucson, AZ, USA 8 Department of Physics, University of Texas at Arlington, Arlington, TX, USA 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, USA 12 (a) Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey; (b) Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey; (c) Department of Physics, Bogazici University, Istanbul, Turkey; (d) Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 15 (a) Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China; (b) 123 Eur. Phys. J. C (2019) 79:733 Page 41 of 45 733 Physics Department, Tsinghua University, Beijing, China; (c) Department of Physics, Nanjing University, Nanjing, China; (d) University of Chinese Academy of Science (UCAS), 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, USA 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham, UK 22 Centro de Investigaciónes, Universidad Antonio Nariño, Bogota, Colombia 23 (a) Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy; (b) INFN Sezione di Bologna, Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston, MA, USA 26 Department of Physics, Brandeis University, Waltham, MA, USA 27 (a) Transilvania University of Brasov, Brasov, Romania; (b) Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania; (c) Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania; (d) National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca, Romania; (e) University Politehnica Bucharest, Bucharest, Romania; (f) West University in Timisoara, Timisoara, Romania 28 (a) Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia; (b) Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton, NY, USA 30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 31 Cavendish Laboratory, University of Cambridge, Cambridge, UK 32 (a) Department of Physics, University of Cape Town, Cape Town, South Africa; (b) Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa; (c) School of Physics, University of the Witwatersrand, Johannesburg, South Africa 33 Department of Physics, Carleton University, Ottawa, ON, Canada 34 (a) Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - Université Hassan II, Casablanca, Morocco; (b) Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat, Morocco; (c) Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA, Marrakech, Morocco; (d) Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda, Morocco; (e) Faculté des sciences, Université Mohammed V, Rabat, Morocco 35 CERN, Geneva, Switzerland 36 Enrico Fermi Institute, University of Chicago, Chicago, IL, USA 37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 38 Nevis Laboratory, Columbia University, Irvington, NY, USA 39 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 40 (a) Dipartimento di Fisica, Università della Calabria, Rende, Italy; (b) INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Frascati, Italy 41 Physics Department, Southern Methodist University, Dallas, TX, USA 42 Physics Department, University of Texas at Dallas, Richardson, TX, USA 43 (a) Department of Physics, Stockholm University, Stockholm, Sweden; (b) Oskar Klein Centre, Stockholm, Sweden 44 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 45 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 46 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany 47 Department of Physics, Duke University, Durham, NC, USA 48 SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK 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, Geneva, Switzerland 123 733 Page 42 of 45 Eur. Phys. J. C (2019) 79:733 53 (a) Dipartimento di Fisica, Università di Genova, Genoa, Italy; (b) INFN Sezione di Genova, Genoa, Italy 54 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 55 SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow, UK 56 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, MA, USA 58 (a) Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, China; (b) Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China; (c) School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai, China; (d) Tsung-Dao Lee Institute, Shanghai, China 59 (a) Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany; (b) Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 61 (a) Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b) Department of Physics, University of Hong Kong, Hong Kong, China; (c) Department of Physics and Institute for Advanced Study, 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, USA 64 (a) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy; (b) ICTP, Trieste, Italy; (c) Dipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine, Italy 65 (a) INFN Sezione di Lecce, Lecce, Italy; (b) Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 66 (a) INFN Sezione di Milano, Milan, Italy; (b) Dipartimento di Fisica, Università di Milano, Milan, Italy 67 (a) INFN Sezione di Napoli, Naples, Italy; (b) Dipartimento di Fisica, Università di Napoli, Naples, Italy 68 (a) INFN Sezione di Pavia, Pavia, Italy; (b) Dipartimento di Fisica, Università di Pavia, Pavia, Italy 69 (a) INFN Sezione di Pisa, Pisa, Italy; (b) Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 70 (a) INFN Sezione di Roma, Rome, Italy; (b) Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy 71 (a) INFN Sezione di Roma Tor Vergata, Rome, Italy; (b) Dipartimento di Fisica, Università di Roma Tor Vergata, Rome, Italy 72 (a) INFN Sezione di Roma Tre, Rome, Italy; (b) Dipartimento di Matematica e Fisica, Università Roma Tre, Rome, Italy 73 (a) INFN-TIFPA, Povo, Italy; (b) Università degli Studi di Trento, Trento, Italy 74 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 75 University of Iowa, Iowa City, IA, USA 76 Department of Physics and Astronomy, Iowa State University, Ames, IA, USA 77 Joint Institute for Nuclear Research, Dubna, Russia 78 (a) Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, Brazil; (b) Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil; (c) Universidade Federal de São João del Rei (UFSJ), São João del Rei, Brazil; (d) Instituto de Física, 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 (a) AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Kraków, Poland; (b) Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland 82 Institute of Nuclear Physics Polish Academy of Sciences, Kraków, 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 Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 87 Physics Department, Lancaster University, Lancaster, UK 88 Oliver Lodge Laboratory, University of Liverpool, Liverpool, UK 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, UK 91 Department of Physics, Royal Holloway University of London, Egham, UK 92 Department of Physics and Astronomy, University College London, London, UK 123 Eur. Phys. J. C (2019) 79:733 Page 43 of 45 733 93 Louisiana Tech University, Ruston, LA, USA 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 Física 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, UK 99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 100 Department of Physics, University of Massachusetts, Amherst, MA, USA 101 Department of Physics, McGill University, Montreal, QC, Canada 102 School of Physics, University of Melbourne, Parkville, VIC, Australia 103 Department of Physics, University of Michigan, Ann Arbor, MI, USA 104 Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA 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, Munich, Germany 113 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), Munich, 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, USA 117 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, The Netherlands 118 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, The Netherlands 119 Department of Physics, Northern Illinois University, DeKalb, IL, USA 120 (a) Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk, Russia; (b) Novosibirsk State University Novosibirsk, Novosibirsk, 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, USA 123 Ohio State University, Columbus, OH, USA 124 Faculty of Science, Okayama University, Okayama, Japan 125 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK, USA 126 Department of Physics, Oklahoma State University, Stillwater, OK, USA 127 Joint Laboratory of Optics, Palacký University, RCPTM, Olomouc, Czech Republic 128 Center for High Energy Physics, University of Oregon, Eugene, OR, USA 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, UK 133 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France 134 Department of Physics, University of Pennsylvania, Philadelphia, PA, USA 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, USA 137 (a) Laboratório de Instrumentação e Física Experimental de Partículas, LIP, Lisbon, Portugal; (b) Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; (c) Departamento de Física, Universidade de Coimbra, Coimbra, Portugal; (d) Centro de Física Nuclear da Universidade de Lisboa, Lisbon, Portugal; (e) Departamento de Física, Universidade do Minho, Braga, Portugal; (f) Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada, Spain; (g) Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 123 733 Page 44 of 45 Eur. Phys. J. C (2019) 79:733 138 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, UK 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, USA 144 (a) Departamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile; (b) Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 145 Department of Physics, University of Washington, Seattle, WA, USA 146 Department of Physics and Astronomy, University of Sheffield, Sheffield, UK 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, USA 151 Physics Department, Royal Institute of Technology, Stockholm, Sweden 152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA 153 Department of Physics and Astronomy, University of Sussex, Brighton, UK 154 School of Physics, University of Sydney, Sydney, Australia 155 Institute of Physics, Academia Sinica, Taipei, Taiwan 156 (a) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia; (b) High Energy Physics Institute, 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 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 (a) TRIUMF, Vancouver, BC, Canada; (b) Department of Physics and Astronomy, 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, USA 168 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA 169 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 170 Department of Physics, University of Illinois, Urbana, IL, USA 171 Instituto de Física 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, UK 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, USA 179 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 180 Department of Physics, Yale University, New Haven, CT, USA 181 Yerevan Physics Institute, Yerevan, Armenia aAlso at Borough of Manhattan Community College, City University of New York, NY, USA. b Also at California State University, East Bay, USA. c Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa. d Also at CERN, Geneva, Switzerland. 123 Eur. Phys. J. C (2019) 79:733 Page 45 of 45 733 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 Física Teorica y del Cosmos, Universidad de Granada, Granada (Spain), Spain. i Also at Departamento de Física, 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, USA. m Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, UK. n Also at Department of Physics, California State University, Fresno CA, USA. o Also at Department of Physics, California State University, Sacramento CA, USA. p Also at Department of Physics, King’s College London, London, UK. q Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia. r Also at Department of Physics, Stanford University, USA. s Also at Department of Physics, University of Fribourg, Fribourg, Switzerland. t Also at Department of Physics, University of Michigan, Ann Arbor MI, USA. u Also at Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy. v Also at Giresun University, Faculty of Engineering, Giresun, Turkey. w Also at Graduate School of Science, Osaka University, Osaka, Japan. x Also at Hellenic Open University, Patras, Greece. y Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania. z Also at II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany. aa Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain. ab Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany. ac Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, The Netherlands. ad Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary. ae Also at Institute of Particle Physics (IPP), Canada. af Also at Institute of Physics, Academia Sinica, Taipei, Taiwan. ag Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan. ah Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia. ai Also at Instituto de Física Teórica de la Universidad Autónoma de Madrid, Spain. aj Also at Istanbul University, Dept. of Physics, Istanbul, Turkey. ak Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France. al Also at Louisiana Tech University, Ruston LA, USA. am Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France. an Also at Manhattan College, New York NY, USA. 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 Near East University, Nicosia, North Cyprus, Mersin, Turkey. ar Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. as Also at School of Physics, Sun Yat-sen University, Guangzhou, China. at Also at The City College of New York, New York NY, USA. au Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China. av Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia. aw Also at TRIUMF, Vancouver BC, Canada. ax Also at Universita di Napoli Parthenope, Napoli, Italy. ∗ Deceased 123