Eur. Phys. J. C (2019) 79:120 https://doi.org/10.1140/epjc/s10052-019-6540-y Regular Article - Experimental Physics A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment ATLAS Collaboration CERN, 1211 Geneva 23, Switzerland Received: 20 July 2018 / Accepted: 21 December 2018 / Published online: 6 February 2019 © CERN for the benefit of the ATLAS collaboration 2019 Abstract This paper describes a strategy for a general Searches that have been performed to date do not fully search used by the ATLAS Collaboration to find potential cover the enormous parameter space of masses, cross- indications of new physics. Events are classified according sections and decay channels of possible new particles. Sig- to their final state into many event classes. For each event nals might be hidden in kinematic regimes and final states class an automated search algorithm tests whether the data that have remained unexplored. This motivates a model- are compatible with the Monte Carlo simulated expectation independent1 analysis to search for physics beyond the Stan- in several distributions sensitive to the effects of new physics. dard Model (BSM) in a structured, global and automated way, The significance of a deviation is quantified using pseudo- where many of the final states not yet covered can be probed. experiments. A data selection with a significant deviation General searches without an explicit BSM signal assump- defines a signal region for a dedicated follow-up analysis tion have been been performed by the DØ Collaboration [1– with an improved background expectation. The analysis of 4] at the Tevatron, by the H1 Collaboration [5,6] at HERA, the data-derived signal regions on a new dataset allows a sta- and by the CDF Collaboration [7,8] at the Tevatron. At the tistical interpretation without the large look-elsewhere effect. LHC, preliminary versions of such searches have been per- √ The sensitivity of the approach is discussed using Standard formed by the ATLAS Collaboration at s = 7, 8 and √ Model processes and benchmark signals of new physics. 13 TeV, and by the CMS Collaboration at s = 7 and 8 TeV. As an example, results are shown for 3.2 fb−1 of proton– This paper outlines a strategy employed by the ATLAS proton collision data at a centre-of-mass energy of 13 TeV Collaboration to search in a systematic and (quasi-)model- collected with the ATLAS detector at the LHC in 2015, in independent way for deviations of the data from the SM pre- which more than 700 event classes and more than 105 regions diction. This approach assumes only generic features of the have been analysed. No significant deviations are found and potential BSM signals. Signal events are expected to have consequently no data-derived signal regions for a follow-up reconstructed objects with relatively large momentum trans- analysis have been defined. verse to the beam axis. The main objective of this strategy is not to finally assess the exact level of significance of a deviation with all available data, but rather to identify with 1 Introduction a first dataset those phase-space regions where significant deviations of the data from SM prediction are present for a Direct searches for unknown particles and interactions are further dedicated analysis. The observation of one or more one of the primary objectives of the physics programme at significant deviations in some phase-space region(s) serves the Large Hadron Collider (LHC). The ATLAS experiment as a trigger to perform dedicated and model-dependent anal- at the LHC has thoroughly analysed the Run 1 pp collision yses where these ‘data-derived’ phase-space region(s) can be dataset (recorded in 2010–2012) and roughly a quarter of the used as signal regions. Such an analysis can then determine expected Run 2 dataset (2015–2018). No evidence of physics the level of significance using a second dataset. The main beyond the Standard Model (SM) has been found in any of advantage of this procedure is that it allows a large num- the searches performed so far. ber of phase-space regions to be tested with the available 1 ‘Model-independent’ refers to the absence of a beyond the Standard Model signal assumption. The analysis depends on the Standard Model  e-mail: atlas.publications@cern.ch prediction. 123 120 Page 2 of 45 Eur. Phys. J. C (2019) 79:120 resources, thereby minimizing the possibility of missing a ger criteria, and are classified according to the type and multi- signal for new physics, while simultaneously maintaining a plicity of reconstructed objects with high transverse momen- low false discovery rate by testing the data-derived signal tum ( pT ). Objects that can be considered in the classification region(s) on an independent dataset in a dedicated analysis. are those typically used to characterize hadron collisions such The dedicated analysis with data-derived signal regions also as electrons, muons, τ -leptons, photons, jets, b-tagged jets allows an improved background prediction. and missing transverse momentum. More complex objects, In this approach, events are first classified into different which were not implemented in the example described in (exclusive) categories, labelled with the multiplicity of final- Sect. 3, could also be considered. Examples are resonances state objects (e.g. muons, electrons, jets, missing transverse reconstructed by a specific decay (e.g. Z or Higgs bosons momentum, etc.) in an event. These final-state categories decaying into two or four isolated leptons respectively, or are then automatically analysed for deviations of the data decaying hadronically and giving rise to large radius jets with from the SM prediction in several BSM-sensitive distribu- substructure) and displaced vertices. Event classes (or chan- tions using an algorithm that locates the region of largest nels) are then defined as the set of events with a given number excess or deficit. Sensitivity tests for specific signal mod- of reconstructed objects for each type, e.g. two muons and a els are performed to demonstrate the effectiveness of this jet. approach. The methodology has been applied to a subset of Monte Carlo (MC) simulations are used to estimate the √ the s = 13 TeV proton–proton collision data as reported in expected event counts from SM processes. To allow the inves- this paper. The data were collected with the ATLAS detec- tigation of signal regions with a low number of expected tor in 2015, and correspond to an integrated luminosity of events it is important that the equivalent integrated luminos- 3.2 fb−1 . ity of the MC samples significantly exceeds that of the data, The paper is organized as follows: the general analysis and that all relevant background processes are included, in strategy is outlined in Sect. 2, while Sect. 3 provides specific particular rare processes which might dominate certain multi- details about its application to the ATLAS 2015 pp collision object event classes. dataset. Conclusions are given in Sect. 4. 2.2 Step 2: Systematic uncertainties and validation 2 Strategy The particular nature of this analysis, in which a large number of final states are explored, makes the definition of control The analysis strategy assumes that a signal of unknown ori- and validation regions difficult. In searches for BSM physics gin can be revealed as a statistically significant deviation of at the LHC, control regions are used to constrain MC-based the event counts in the data from the expectation in a specific background predictions with auxiliary measurements. Vali- data selection. A data selection can be any set of requirements dation regions are used to test the validity of the background on objects or variables needed to define a signal region (e.g. model prediction with data. an event class or a specific range in one or multiple observ- The simplest way to construct a background model is to ables). In order to search for these signals a large variety of obtain the background expectation from the MC prediction data selections need to be tested. This requires a high degree including the corresponding theoretical and experimental of automation and a categorization of the data events accord- uncertainties. This approach, which is applied in the example ing to their main features. The main objective of this analysis in Sect. 3, has the advantage that it prevents the absorption is to identify selections for which the data deviates signifi- of BSM signal contributions into a rescaling of the SM pro- cantly from the SM expectation. These selections can then be cesses. Another possible approach is to automatically define, applied as data-derived signal regions in a dedicated analysis for each data selection and algorithmic hypothesis test, statis- to determine the level of significance using a new dataset. tically independent control selections. The data in the control This has the advantage of a more reliable background expec- selections can be used to rescale the MC background pre- tation, which should allow an increase in signal sensitivity dictions and to constrain the systematic uncertainties. This compared to a strategy that only relies on Monte Carlo expec- comes at the price of reduced sensitivity for the case in which tations with a typically conservative evaluation of uncertain- a BSM model predicts a simultaneous effect in the signal ties. The strategy is divided into the seven steps described region and control region, which would be absorbed in the below. rescaling. To verify the proper modelling of the SM background 2.1 Step 1: Data selection and Monte Carlo simulation processes, several validation distributions are defined using inclusive selections for which observable signals for new The recorded data are reconstructed via the ATLAS software physics are excluded. If these validation distributions show chain. Events are selected by applying event-quality and trig- problems in the MC modelling, either corrections to the 123 Eur. Phys. J. C (2019) 79:120 Page 3 of 45 120 MC backgrounds are applied or the affected event class is events for a given selection, P(n ≤ Nobs ) is the probability excluded. of observing no more than the number of events observed Uncertainties in the background estimate arise from exper- in the data and P(n ≥ Nobs ) is the probability of observ- imental effects, and the theoretical accuracy of the prediction ing at least the number of events observed in the data. The of the (differential) cross-section and acceptance of the MC quantity NSM is the expectation for the number of events simulation. Their effect is evaluated for all contributing back- with its total uncertainty δ NSM for a given selection. The ground processes as well as for benchmark signals. convolution of the Poisson pmf (with mean x) with a Gaus- sian probability density function (pdf), G(x; NSM , δ NSM ) 2.3 Step 3: Sensitive variables and search algorithm with mean NSM and width δ NSM , takes the effect of both non-negligible systematic uncertainties and statistical uncer- Distributions of observables in the form of histograms are tainties into account.2 If the Gaussian pdf G is replaced by a investigated for all event classes considered in the analysis. Dirac delta function δ(x − NSM ) the estimator p0 results in Observables are included if they have a high sensitivity to a the usual Poisson probability. The selection with the largest wide range of BSM signals. The total number of observables deviation identified by the algorithm is defined as the selec- considered is, however, restricted to a few to avoid a large tion giving the smallest p0 -value. The smallest p0 for a given increase in the number of hypothesis tests, as the latter also channel is defined as pchannel , which therefore corresponds increases the rate of deviations from background fluctuations. to the local p0 -value of the largest deviation in that channel. In high-energy physics this effect is commonly known as the Data selections are not considered in the scan if large ‘trial factor’ or ‘look-elsewhere effect’. Examples of such uncertainties in the expectation arise due to a lack of MC observables are the effective mass m eff (defined as the sum events, or from large systematic uncertainties. To avoid over- of the scalar transverse momenta of all objects plus the scalar looking potential excesses in these selections the p0 -values missing transverse momentum), the total invariant mass m inv of selections with more than three data events are monitored (defined as the invariant mass of all visible objects), the separately. Single outstanding events with atypical object invariant mass of any combination of objects (such as the multiplicities (e.g. events with 12 muons) are visible as an dielectron invariant mass in events with two electrons and two event class. Single outstanding events in the scanned distri- muons), event shape variables such as thrust [9,10] or even butions are monitored separately. more complicated variables such as the output of a machine- The result of scanning the distributions for all event classes learning algorithm. is a list of data selections, one per event class containing the A statistical algorithm is used to scan these distributions largest deviation in that class, and their local statistical signif- for each event class and quantify the deviations of the data icance. Details of the procedure and the statistical algorithm from the SM expectation. The algorithm identifies the data used for the 2015 dataset are explained in Sect. 3.3. selection that has the largest deviation in the distribution of the investigated observable by testing many data selections 2.4 Step 4: Generation of pseudo-experiments to minimize a test statistic. An example of a possible test- statistic which has also been used in the analysis described The probability that for a given observable one or more in Sect. 3, is the local p0 -value, which gives the expected deviations of a certain size occur somewhere in the event probability of observing a fluctuation that is at least as far classes considered is modelled by pseudo-experiments. Each from the SM expectation as the observed number of data pseudo-experiment consists of exactly the same event classes events in a given region, if the experiment were to be repeated: as those considered when applying the search algorithm to   p0 = 2 · min P(n ≤ Nobs ), P(n ≥ Nobs ) , (1) 2 The second term in Eq. (2) gives the probability of observing no  ∞  Nobs −x n events given a negative expectation from downward variations of the e x systematic uncertainties. It can be derived as follows: P(n ≤ Nobs ) = dx G (x; NSM , δ NSM ) · 0 n!    n=0 0  Nobs e−μ μn  0 dx G (x; NSM , δ NSM ) · lim −∞ μ→0 n! + dx G (x; NSM , δ NSM ) , (2) n=0 −∞  0  Nobs  ∞ ∞  = dx G (x; NSM , δ NSM ) · δn0 e−x x n −∞ P(n ≥ Nobs ) = dx G (x; NSM , δ NSM ) · , n=0 0 n!  0 n=Nobs = dx G (x; NSM , δ NSM ) , (3) −∞ where μ is the mean of the Poisson pmf and δn0 = where n is the independent variable of the Poisson probabil- {1 if n = 0, 0 if n = 0} is the Kronecker delta. In Eq. (3) this term ity mass function (pmf), Nobs is the observed number of data vanishes for Nobs > 0. 123 120 Page 4 of 45 Eur. Phys. J. C (2019) 79:120 probability (Pexp,i ) of obtaining at least one pchannel -value (i = 1) smaller than 10−4 from data in the absence of a signal is about 15%, or Pexp,1 (10−4 ) = 0.15. To estimate the probability of observing deviations of a given size in at least two or three different event classes, the second or third smallest pchannel -value of a pseudo-experiment is com- pared with a given pmin threshold. From Fig. 1 it follows for instance that 2% of the pseudo-experiments have at least three pchannel -values smaller than 10−4 . Consequently, the proba- bility of obtaining a third smallest pchannel -value smaller than 10−4 from data in the absence of a signal is about 2%, or Pexp,3 (10−4 ) = 0.02 In Fig. 1 a horizontal dotted line is drawn at a fraction of pseudo-experiments of 5% and corresponding vertical dot- Fig. 1 The fractions of pseudo-experiments (Pexp,i ( pmin )) in the m inv ted lines are drawn at the three pmin thresholds. The obser- scan, which have at least one, two or three pchannel -values smaller than vation of one, two or three pchannel -values in data below a given threshold ( pmin ). Pseudo-datasets are generated from the SM the corresponding pmin threshold, i.e. an observation with expectation. Dotted lines are drawn at Pexp,i = 5% and at the corre- sponding − log10 ( pmin )-values a Pexp,i < 0.05, promotes the selections that yielded these deviations to signal regions that can be tested in a new dataset. data. However, the data counts are replaced by pseudo-data counts which are generated from the SM expectation using an 2.5 Step 5: Evaluation of the sensitivity MC technique. Pseudo-data distributions are produced tak- ing into account both statistical and systematic uncertainties The sensitivity of the procedure to a priori unspecified BSM by drawing pseudo-random data counts for each bin from the signals can be evaluated with two different methods that convolved pmf used in Eqs. (1)–(3) to compute a p0 -value. either use a modified background estimation through the Correlations in the uncertainties of the SM expectation removal of SM processes or in which signal contributions affect the chance of observing one or more deviations of a are added to the pseudo-data sample. given size. The effect of correlations between bins of the same In the first method, a rare SM process (with either a low distribution or between distributions of different event classes cross-section or a low reconstruction efficiency) is removed are therefore taken into account when generating pseudo-data from the background model. The search algorithm is applied for pseudo-experiments. Correlations between distributions again to test the data or ‘signal’ pseudo-experiments gener- of different observables are not taken into account, since the ated from the unmodified SM expectation, against the mod- results obtained for different observables are not combined ified background expectation. The data samples would be in the interpretation. expected to reveal excesses relative to the modified back- The search algorithm is then applied to each of the dis- ground prediction. tributions, resulting in a pchannel -value for each event class. In the second method, pseudo-experiments are used to test The pchannel distributions of many pseudo-experiments and the sensitivity of the analysis to benchmark signal models of their statistical properties can be compared with the pchannel new physics. The prediction of a model is added to the SM distribution obtained from data to interpret the test statistics prediction, and this modified expectation is used to generate in a frequentist manner. The fraction of pseudo-experiments ‘signal’ pseudo-experiments. The search algorithm is applied having one of the pchannel -values smaller than a given value to the pseudo-experiments and the distribution of pchannel - pmin indicates the probability of observing such a deviation values is derived. by chance, taking into account the number of selections and To provide a figure of merit for the sensitivity of the event classes tested. analysis, the fraction of ‘signal’ pseudo-experiments with To illustrate this, Fig. 1 shows three cumulative distribu- Pexp,i < 5% for i = 1, 2, 3 is computed. tions of pchannel -values from pseudo-experiments. The num- ber of event classes (686) and the m inv distributions used to 2.6 Step 6: Results generate these pseudo-experiments coincide with the exam- ple application in Sect. 3. The distribution in Fig. 1 with cir- Finding one or more deviations in the data with Pexp,i < 5% cular markers is the fraction of pseudo-experiments with at triggers a dedicated analysis that uses the data selection in least one pchannel -value smaller than pmin . For example, about which the deviation is observed as a signal region (step 7). If 15% of the pseudo-experiments have at least one pchannel - no significant deviations are found, the outcome of the anal- value smaller than pmin = 10−4 . Therefore, the estimated ysis technique includes information such as: the number of 123 Eur. Phys. J. C (2019) 79:120 Page 5 of 45 120 events and expectation per event class, a comparison of the • The approach is broad, and the scanned distributions can data with the SM expectation in the distributions of observ- be used to probe the overall description of the data by the ables considered, the scan results (i.e. the location and the event generators for many SM processes. local p0 -value of the largest deviation per event class) and the • The probability of a deviation occurring in any of the comparison with the expectation from pseudo-experiments. many different event classes under study can be deter- mined with pseudo-experiments, resulting in a truly 2.7 Step 7 (only in the case of Pexp,i < 5%): Dedicated global interpretation of the probability of finding a devi- analysis of deviation ation within an experiment such as ATLAS. Dedicated analysis on original dataset Deviations are inves- Disadvantages: tigated using methods similar to those of a conventional analysis. In particular, the background prediction is deter- mined using control selections to control and validate the • The outcome depends on the MC-based description background modelling. Such a procedure further constrains of physics processes and simulations of the detector the background expectation and uncertainty, and reduces the response. Event classes in which the majority of the dependence on simulation. If such a re-analysis of the region events contain misreconstructed objects are typically results in an insignificant deviation, it can be inferred that poorly modelled by MC simulation and might need to the deviation seen before was due to mismodellings or not be excluded from the analysis. Although step 2 validates well-enough understood backgrounds. the description of the data by the MC simulation, there is still a possibility of triggering false positives due to Dedicated analysis on an independent dataset If a deviation an MC mismodelling in a corner of phase space. Step persists in a dedicated analysis using the original dataset, the 7 aims to minimize this by reducing the dependence on data selection in which the deviation is observed defines a MC simulations in a dedicated analysis performed for data-derived signal region that is tested in an independent each significant deviation. In future implementations a new dataset with a similar or larger integrated luminosity. At better background model could be constructed with the this point, a particular model of new physics can be used to help of control regions or data-derived fitting functions. interpret the result of testing the data-derived signal region. This might allow the detection of excesses that are small Since the signal region is known, the corresponding data can compared to the uncertainties in the MC-based descrip- be excluded (‘blinded’) from the analysis until the very end tion of the SM processes. to minimize any possible bias in the analysis. Additionally, • Since this analysis is not optimized for a specific class of since only a few optimized hypothesis tests are performed BSM signals, a dedicated analysis optimized for a given on the independent dataset, the large look-elsewhere effect BSM signal achieves a larger sensitivity to that signal. due to the large number of hypothesis tests performed in The enormous parameter space of possible signals makes step 3 is not present in the dedicated analysis of the signal an optimized search for each of them impossible. region(s). The assumptions of Gaussian uncertainties for the • The large number of data selections introduce a large background models can also be tested in the dedicated anal- look-elsewhere effect, which reduces the significance of ysis. If the full LHC data yields a significant deviation, the a real signal. Step 7 circumvents this problem since the LHC running time may need to be increased, or the excess final discovery significance is determined with a dedi- may have to be followed up at a future collider. cated analysis of one or a few data selection(s) and a sta- tistically independent dataset. This can yield an improved 2.8 Advantages and disadvantages signal sensitivity if the background uncertainty can be constrained in the dedicated analysis. The features of this strategy lead to several advantages and • Despite being broad, the procedure might miss a certain disadvantages that are outlined below. signal because it does not show a localized excess in one of the studied distributions. This might be overcome with Advantages: better observables, better event classification or modified algorithms, which may then be sensitive to such signals. • It can find unexpected signals for new physics due to the large number of event classes and phase-space regions probed, which may otherwise remain uninvestigated. 3 Application of the strategy to ATLAS data • A relatively small excess in two or three independent data selections, each of which is not big enough to trigger a This section describes the application of the strategy out- dedicated analysis by itself (Pexp,1 > 5%), can trigger lined in the previous section to the 13 TeV pp collision data one in combination (Pexp,2,3 < 5%). recorded by the ATLAS experiment in 2015. 123 120 Page 6 of 45 Eur. Phys. J. C (2019) 79:120 3.1 Step 1: Data selection and Monte Carlo simulation 3.1.2 Monte Carlo samples 3.1.1 ATLAS detector and dataset Monte Carlo simulated event samples [15] are used to describe SM background processes and to model possible The ATLAS detector [11] is a multipurpose particle physics signals. The ATLAS detector is simulated either by a soft- detector with a forward-backward symmetric cylindrical ware system based on Geant4 [16] or by a faster simulation geometry and a coverage of nearly 4π in solid angle.3 based on a parameterization of the calorimeter response and The inner tracking detector (ID) consists of silicon pixel Geant4 for the other detector systems. The impact of detec- and microstrip detectors covering the pseudorapidity region tor conditions on the simulation is typically corrected for as |η| < 2.5, surrounded by a straw-tube transition radiation part of the calibrations and scale factors applied to the recon- tracker which enhances electron identification in the region structed objects. |η| < 2.0. Between Run 1 and Run 2, a new inner pixel To account for additional pp interactions from the same or layer, the insertable B-layer [12], was inserted at a mean nearby bunch crossings, a set of minimum-bias interactions sensor radius of 3.3 cm. The inner detector is surrounded generated using Pythia 8.186 [17], the MSTW2008LO [18] by a thin superconducting solenoid providing an axial 2 T parton distribution function (PDF) set and the A2 set of tuned magnetic field and by a fine-granularity lead/liquid-argon parameters (tune) [19] was superimposed onto the hard- (LAr) electromagnetic calorimeter covering |η| < 3.2. A scattering events to reproduce the observed distribution of steel/scintillator-tile calorimeter provides hadronic coverage the average number of interactions per bunch crossing. in the central pseudorapidity range (|η| < 1.7). The endcap Any further study of time-dependent detector variations and forward calorimeter coverage (1.5 < |η| < 4.9) is com- would be part of the dedicated search following any interest- pleted by LAr active layers with either copper or tungsten as ing deviation. the absorber material. An extensive muon spectrometer with In all MC samples, except those produced by Sherpa [20], an air-core toroid magnet system surrounds the calorimeters. the EvtGen v1.2.0 program [21] was used to model the prop- Three layers of high-precision tracking chambers provide erties of the bottom and charm hadron decays. The SM MC coverage in the range |η| < 2.7, while dedicated fast cham- programs are listed in Table 1 and a detailed explanation can bers provide a muon trigger in the region |η| < 2.4. The be found in Appendix A.1. ATLAS trigger system consists of a hardware-based level-1 In addition to the SM background processes, two possible trigger followed by a software-based high-level trigger [13]. signals are considered as benchmarks. The first benchmark The data used in this analysis were collected by the model considered is the production of a new heavy neutral ATLAS detector during 2015 in pp collisions at the LHC gauge boson of spin 1 (Z  ), as predicted by many exten- with a centre-of-mass energy of 13 TeV and a 25 ns bunch sions of the SM. Here, the specific case of the sequential crossing interval. After applying quality criteria for the beam, extension of the SM gauge group (SSM) [22,23] is consid- data and detector, the available dataset corresponds to an ered, for which the couplings are the same as for the SM integrated luminosity of 3.2 fb−1 . In this dataset, each event Z boson. This process was generated at leading order (LO) includes an average of approximately 14 additional inelastic using Pythia 8.212 with the NNPDF23LO [24] PDF set and pp collisions in the same bunch crossing (pile-up). the A14 tune [25], as a Drell–Yan process, for five different Candidate events are required to have a reconstructed resonant masses, covering the range from 2 TeV to 4 TeV, vertex [14], with at least two associated tracks with pT > in steps of 0.5 TeV. The considered decays of Z  bosons are 400 MeV. The vertex with the highest sum of squared trans- inclusive, covering the full range of lepton and quark pairs. verse momenta of the tracks is considered to be the primary Interference effects with SM Drell–Yan production are not vertex. included, and the Z  boson is required to decay into fermions only. The second signal considered is the supersymmetric [26– 31] production of gluino pairs through strong interactions. 3 ATLAS uses a right-handed coordinate system with its origin at the The gluinos are assumed to decay promptly into a pair of nominal interaction point in the centre of the detector. The positive top quarks and an almost massless neutralino via an off- x-axis is defined by the direction from the interaction point to the cen- tre of the LHC ring, with the positive y-axis pointing upwards, while shell top squark g̃ → tt χ̃10 . Samples for this process were the beam direction defines the z-axis. Cylindrical coordinates (r , φ) generated at LO with up to two additional partons using are used in the transverse plane, φ being the azimuthal angle around MG5_aMC@NLO 2.2.2 [32] with the CTEQ6L1 [33] PDF the z-axis. The pseudorapidity η is defined in terms of the polar angle set, interfaced to Pythia 8.186 with the A14 tune. The match- θ by η = − ln tan(θ/2). The angular distance is defined as R =  ing with the parton shower was done using the CKKW-L [34] ( η)2 + ( φ)2 . Rapidity is defined as y = 0.5·ln[(E+ pz )/(E− pz )] where E denotes the energy and pz is the component of the momentum prescription, with a matching scale set to one quarter of along the beam direction. the pair-produced resonance mass. The signal cross-sections 123 Table 1 A summary of the MC samples used in the analysis to model SM background processes. For each sample the corresponding generator, matrix element (ME) accuracy, parton shower, cross-section normalization accuracy, PDF set and tune are indicated. Details are given in Appendix A.1. Samples with ‘data’ in the ‘cross-section normalization’ column are scaled to data as described in Sect. 3.2.3. Z refers to γ ∗ / Z Physics process Generator ME accuracy Parton shower Cross-section normalization PDF set Tune Eur. Phys. J. C (2019) 79:120 W (→ ν) + jets Sherpa 2.1.1 0,1,2j@NLO + 3,4j@LO Sherpa 2.1.1 NNLO NLO CT10 Sherpa default Z (→ + −) + jets Sherpa 2.1.1 0,1,2j@NLO + 3,4j@LO Sherpa 2.1.1 NNLO NLO CT10 Sherpa default Z / W (→ q q̄) + jets Sherpa 2.1.1 1,2,3,4j@LO Sherpa 2.1.1 NNLO NLO CT10 Sherpa default Z/ W + γ Sherpa 2.1.1 0,1,2,3j@LO Sherpa 2.1.1 NLO NLO CT10 Sherpa default Z/ W + γ γ Sherpa 2.1.1 0,1,2,3j@LO Sherpa 2.1.1 NLO NLO CT10 Sherpa default γ + jets Sherpa 2.1.1 0,1,2,3,4j@LO Sherpa 2.1.1 data NLO CT10 Sherpa default γ γ + jets Sherpa 2.1.1 0,1,2j@LO Sherpa 2.1.1 data NLO CT10 Sherpa default γ γ γ + jets MG5_aMC@NLO 2.3.3 0,1j@LO Pythia 8.212 LO NNPDF23LO A14 t t¯ Powheg- Box v2 NLO Pythia 6.428 NNLO+NNLL NLO CT10 Perugia 2012 t t¯ + W MG5_aMC@NLO 2.2.2 0,1,2j@LO Pythia 8.186 NLO NNPDF2.3LO A14 t t¯ + Z MG5_aMC@NLO 2.2.2 0,1j@LO Pythia 8.186 NLO NNPDF2.3LO A14 t t¯ + W W MG5_aMC@NLO 2.2.2 LO Pythia 8.186 NLO NNPDF2.3LO A14 t t¯ + γ MG5_aMC@NLO 2.2.2 LO Pythia 8.186 LO NNPDF2.3LO A14 t t¯ + bb̄ Sherpa 2.2.0 NLO Sherpa 2.2.0 NLO NLO CT10f4 Sherpa default Single-top (t-channel) Powheg- Box v1 NLO Pythia 6.428 app. NNLO NLO CT10f4 Perugia 2012 Single-top (s- and W t-channel) Powheg- Box v2 NLO Pythia 6.428 app. NNLO NLO CT10 Perugia 2012 tZ MG5_aMC@NLO 2.2.2 LO Pythia 8.186 LO NNPDF2.3LO A14 3-top MG5_aMC@NLO 2.2.2 LO Pythia 8.186 LO NNPDF2.3LO A14 4-top MG5_aMC@NLO 2.2.2 LO Pythia 8.186 NLO NNPDF2.3LO A14 WW Sherpa 2.1.1 0j@NLO + 1,2,3j@LO Sherpa 2.1.1 NLO NLO CT10 Sherpa default WZ Sherpa 2.1.1 0j@NLO + 1,2,3j@LO Sherpa 2.1.1 NLO NLO CT10 Sherpa default ZZ Sherpa 2.1.1 0,1j@NLO + 2,3j@LO Sherpa 2.1.1 NLO NLO CT10 Sherpa default Multijets Pythia 8.186 LO Pythia 8.186 data NNPDF2.3LO A14 Higgs (ggF/VBF) Powheg- Box v2 NLO Pythia 8.186 NNLO NLO CT10 AZNLO Higgs (t t¯ H ) MG5_aMC@NLO 2.2.2 NLO Herwig++ NNLO NLO CT10 UEEE5 Higgs (W/Z H ) Pythia 8.186 LO Pythia 8.186 NNLO NNPDF2.3LO A14 Tribosons Sherpa 2.1.1 0,1,2j@LO Sherpa 2.1.1 NLO NLO CT10 Sherpa default 123 Page 7 of 45 120 120 Page 8 of 45 Eur. Phys. J. C (2019) 79:120 were calculated at next-to-leading order (NLO) in the strong 3.1.4 Event selection and classification coupling constant, adding the resummation of soft gluon emission at next-to-leading-logarithm (NLL) accuracy [35– The events are divided into mutually exclusive classes that are 37]. labelled with the number and type of reconstructed objects listed in Table 2. The division can be regarded as a classifi- 3.1.3 Object reconstruction cation according to the most important features of the event. The classification includes all possible final-state configura- Reconstructed physics objects considered in the analysis are: tions and object multiplicities, e.g. if a data event with seven prompt and isolated electrons (e), muons (μ) and photons reconstructed muons and no other objects is found, it is clas- (γ ), as well as b-jets (b) and light (non-b-tagged) jets ( j) sified in a ‘7-muon’ event class (7μ). Similarly an event with reconstructed with the anti-kt algorithm [38] with radius missing transverse momentum, two muons, one photon and parameter R = 0.4, and large missing transverse momentum four jets is classified and considered in the corresponding (E Tmiss ). Table 2 lists the reconstructed physics objects along event class denoted E Tmiss 2μ1γ 4 j. with their pT and pseudorapidity requirements. Jets and elec- All events contributing to a particular event class are also trons misidentified as hadronically decaying τ -leptons are required to be selected by a trigger from a corresponding difficult to model with the MC-based approach used in this class of triggers by imposing a hierarchy in the event selec- analysis. Therefore, the identification of hadronically decay- tion. This avoids ambiguities in the application of trigger effi- ing τ -leptons is not considered; they are mostly reconstructed ciency corrections to MC simulations and avoids variations as light jets. Details of the object reconstruction can be found in the acceptance within an event class. The flow diagram in Appendix B. in Fig. 2 gives a graphical representation of the trigger and After object identification, overlaps between object can- offline event selection, based on the class of the event. Since didates  are resolved using the distance variable R y = the thresholds for the single-photon and single-jet triggers ( y) + ( φ)2 . If an electron and a muon share the same 2 are higher than the pT requirements in the photon and jet ID track, the electron is removed. Any jet within a dis- object selection, an additional reconstruction-level pT cut is tance R y = 0.2 of an electron candidate is discarded, imposed to avoid trigger inefficiencies. For the other trig- unless the jet has a value of the b-tagging MV2c20 discrimi- gers, the pT requirements in the object definitions exceed the nant [39,40] larger than that corresponding to approximately trigger thresholds by a sufficient margin to avoid additional 85% b-tagging efficiency, in which case the electron is dis- trigger inefficiencies. Electrons are considered before muons carded since it probably originated from a semileptonic b- in the event selection hierarchy because the electron trigger hadron decay. Any remaining electron within R y = 0.4 efficiency is considerably higher compared to the muon trig- of a jet is discarded. Muons within R y = 0.4 of a ger efficiency. jet are also removed. However, if the jet has fewer than Events with E Tmiss > 200 GeV are required to pass the miss E T trigger which becomes fully efficient at 200 GeV, oth- three associated tracks, the muon is kept and the jet is discarded instead to avoid inefficiencies for high-energy erwise they are rejected and not considered for further event muons undergoing significant energy loss in the calorime- selection. If the event has E Tmiss < 200 GeV but contains ter. If a photon candidate is found within R y = 0.4 of an electron with pT > 25 GeV it is required to pass the a jet, the jet is discarded. Photons within a cone of size single-electron trigger. However, events with more than one R y = 0.4 around an electron or muon candidate are electron with pT > 25 GeV or with an additional muon discarded. with pT > 25 GeV can be selected by the dielectron trig- The missing transverse momentum (with magnitude ger or electron-muon trigger respectively if the event fails E Tmiss ) is defined as the negative vector sum of the trans- to pass the single-electron trigger. Events with a muon with verse momenta of all selected and calibrated physics objects pT > 25 GeV but no reconstructed electrons or large E Tmiss (electrons, photons, muons and jets) in the event, with an are required to pass the single-muon trigger. If the event has additional soft-term [41]. The soft-term is constructed from more than one muon with pT > 25 GeV and fails to pass all tracks that are not associated with any physics object, but the single-muon trigger, it can additionally be selected by are associated with the primary vertex. The missing trans- the dimuon trigger. Remaining events with a photon with verse momentum is reconstructed for all events; however, pT > 140 GeV or two photons with pT > 50 GeV are separate analysis channels are constructed for events with required to pass the single-photon or diphoton trigger, respec- E Tmiss > 200 GeV. These events are taken exclusively from tively. Finally, any remaining event with no large E Tmiss , lep- the E Tmiss trigger. tons, or photons, but containing a jet with pT > 500 GeV is required to pass the single-jet trigger. 123 Eur. Phys. J. C (2019) 79:120 Page 9 of 45 120 Table 2 The physics objects Object Label pT (min) (GeV) Pseudorapidity used for classifying the events, with their corresponding label, Isolated electron e 25 |η| < 1.37 or 1.52 < |η| < 2.47 minimum pT requirement, and pseudorapidity requirement Isolated muon μ 25 |η| < 2.7 Isolated photon γ 50 |η| < 1.37 or 1.52 < |η| < 2.37 b-tagged jet b 60 |η| < 2.5 Light (non-b-tagged) jet j 60 |η| < 2.8 Missing transverse momentum E Tmiss 200 Fig. 2 Flow diagram for the trigger and offline event selection strategy. The offline requirements are shown on the left of the dashed line and the trigger requirements are shown on the right of the dashed line 123 120 Page 10 of 45 Eur. Phys. J. C (2019) 79:120 In addition to the thresholds imposed by the trigger, a the modelling of the shape of the differential cross-sections. further selection is applied to event classes with E Tmiss < In order to derive the modelling uncertainties, either varia- 200 GeV containing one lepton or one electron and one muon tions of the QCD factorization, renormalization, resumma- and possibly additional photons or jets (1μ + X , 1e + X and tion and merging scales are used or comparisons of the nomi- 1μ1e + X ), to reduce the overall data volume. In these event nal MC samples with alternative ones are used. For some SM classes, one lepton is required to have pT > 100 GeV if the processes additional modelling uncertainties are included. event has less than three jets with pT > 60 GeV. Appendix A.2 describes all theoretical uncertainties consid- To suppress sources of fake E Tmiss , additional requirements ered for the various SM processes. The total uncertainty is are imposed on events to be classified in E Tmiss categories. The taken as the sum in quadrature of the two components and ratio of E Tmiss to m eff is required to be greater than 0.2, and the the statistical uncertainty of the MC prediction. minimum azimuthal separation between the E Tmiss direction and the three leading reconstructed jets (if present) has to be 3.2.2 Validation procedures greater than 0.4, otherwise the event is rejected. The evaluated SM processes, together with their standard 3.2 Step 2: Systematic uncertainties and validation selection cuts and the studied validation distributions, are detailed in Table 3. These validation distributions rely on 3.2.1 Systematic uncertainties inclusive selections to probe the general agreement between data and simulation and are evaluated in restricted ranges Experimental uncertainties The dominant experimental sys- where large new-physics contributions have been excluded tematic uncertainties in the SM expectation for the different by previous direct searches. event classes typically are the jet energy scale (JES) and There are some cases in which the validation procedure resolution (JER) [42] and the scale and resolution of the finds modelling problems and MC background corrections E Tmiss soft-term. The uncertainty related to the modelling are needed (multijets, γ (γ ) + jets). In other cases, the affected of E Tmiss in the simulation is estimated by propagating the event classes are excluded from the analysis as their SM uncertainties in the energy and momentum scale of each of expectation dominantly arises from object misidentification the objects entering the calculation, with an additional uncer- (e.g. jets reconstructed as electrons) which is poorly modelled tainty in the resolution and scale of the soft-term [41]. The in MC simulation. The excluded classes are: 1e1 j, 1e2 j, uncertainties in correcting the efficiency of identifying jets 1e3 j, 1e4 j, 1e1b, 1e1b1 j, 1e1b2 j, 1e1b3 j. Event classes containing b-hadrons in MC simulations are determined in containing a single object, as well as those containing only data samples enriched in top quark decays, and in simulated E Tmiss and a lepton are also discarded from the analysis due events [39]. Leptonic decays of J/ψ mesons and Z bosons in to difficulties in modelling final states with one high energy data and simulation are exploited to estimate the uncertainties object recoiling against many soft (non-reconstructed) ones. in lepton reconstruction, identification, momentum/energy scale and resolution, and isolation criteria [43–45]. Photon reconstruction and identification efficiencies are evaluated 3.2.3 Corrections to the MC background from samples of Z → ee and Z + γ events [45,46]. The luminosity measurement was calibrated during dedicated The MC samples for multijet and γ + jets production, while beam-separation scans, using the same methodology as that giving a good description of kinematic variables, predict an described in Ref. [47]. The uncertainty of this measurement overall cross-section and a jet multiplicity distribution that is found to be 2.1%. disagrees with data. Following step 2, correction procedures In total, 35 sources of experimental uncertainties are iden- were applied. tified pertaining to one or more physics objects considered. In classes containing only j and b the multijet MC samples For each source the one-standard-deviation (1σ ) confidence are scaled to data with normalization factors ranging between interval (CI) is propagated to a 1σ CI around the nominal SM approximately 0.8 and 1.2. The normalization factors are expectation. The total experimental uncertainty of the SM derived separately in each exclusive jet multiplicity class by expectation is obtained from the sum in quadrature of these equating the expected total number of events to the observed 35 1σ CIs and the uncertainty of the luminosity measurement. number of events. Multijet production in other channels are not rescaled and found to be described by the MC samples Theoretical modelling uncertainties Two different sources within the theoretical uncertainties. If a channel contains less of uncertainty in the theoretical modelling of the SM produc- than four data events, no modifications are made. tion processes are considered. A first uncertainty is assigned For γ + jets event classes the same rescaling procedure to account for our knowledge of the cross-sections for the is applied to classes with exactly one photon, no leptons or inclusive processes. A second uncertainty is used to cover E Tmiss , and any number of jets. 123 Eur. Phys. J. C (2019) 79:120 Page 11 of 45 120 Table 3 A summary of the SM processes and their inclusive selections is defined as [2 pT ( ) E Tmiss (1 − cos φ( , E Tmiss ))]1/2 . N ((b-)jets) is used to validate the background modelling. For each selection the pT , η, the number of (b-)jets in an event. For the distance variables R and and φ distributions of the objects used in the selection and of additional φ, the two instances of the objects with the minimum distance between jets are included as validation distributions by default. Additional vali- them are used. The HT (jets), m T ( , E Tmiss ), pT ( ) and m inv validation dation distributions are listed per selection. In all cases, ‘jet(s)’ refers to distributions are evaluated in restricted ranges where large new-physics both b-tagged and non-b-tagged jets, except where ‘b-jet’ is mentioned contributions have been excluded by previous direct searches. Same- explicitly. HT (jets) is defined as the scalar pT sum of all the jets in the flavour opposite-charge sign lepton pairs are referred to as SFOS pairs event. Some selections rely on the transverse mass (m T ( , E Tmiss )) which Physics process Event selection Additional validation distributions W (→ ν) + jets 1 lepton, E Tmiss > 25 GeV N (jets) N (b-jets) m T ( , E Tmiss ) HT (jets) and m T ( , E Tmiss ) > 50 GeV R( , jet) φ( , E Tmiss ) φ(jet, E Tmiss ) & N (jets) ≥ 3 HT (jets) & N (b-jets) ≥ 1 m T ( , E Tmiss ) & N (b-jets) ≥ 2 m T ( , E Tmiss ) Z (→ ) + jets 1 SFOS pair N (jets) N (b-jets) m inv ( ) pT ( ) 66 < m inv ( ) < 116 GeV HT (jets) R( , ) R( , jet) & N (jets) ≥ 2 HT (jets) & N (b-jets) ≥ 1 m inv ( ) pT ( ) & N (b-jets) ≥ 2 m inv ( ) pT ( ) W + γ (γ ) Same selection as W (→ ν) + jets Same distributions as W (→ ν) + jets and and 1(2) additional photon(s) R( , γ ) Z + γ (γ ) Same selection as Z (→ ) + jets Same distributions as Z (→ ) + jets and and 1(2) additional photon(s) R( , γ ) γ (γ ) + jets 1(2) photon(s), no leptons N (jets) N (b-jets) HT (jets) m inv (γ γ ) and at least 1(0) jet(s) R(γ , γ ) R(γ , jet) t t¯ → 1 lepton, at least 2 b-jets and at least 2 light jets N (jets) N (b-jets) m inv ( j j) W (→ j j) + 50 < m inv ( j j) < 110 GeV m T ( , E Tmiss ) R(jet, jet) R( , jet) W (→ ν) + Electron channel: m T (e, E Tmiss ) > 50 GeV bb Muon channel: m T (μ, E Tmiss ) > 60 GeV E Tmiss > 40 GeV Diboson WW 1 electron and 1 muon of opposite charge and no jets E Tmiss > 50 GeV WZ 1 SFOS pair ( ) m inv ( ) (SFOS pair(s)) and 1 lepton of different flavour (  ) pT ( ) (SFOS pair(s)) 66 < m inv ( ) < 116 GeV m T (  , E Tmiss ) E Tmiss > 50 GeV and m T ( , E Tmiss ) > 50 GeV R( , ) φ( , E Tmiss ) ZZ 2 SFOS pairs 66 < m inv ( ) < 116 GeV (both SFOS pairs) Multijets At least 2 jets N (jets) N (b-jets) R(jet, jet) No leptons or photons E Tmiss /m eff The Sherpa 2.1.1 MC generator has a known deficiency reweighting [48] is applied to the background events con- in the modelling of E Tmiss due to too large forward jet activity. taining two real photons (γ γ + jets). The diphoton MC This results in a visible mismodelling of the E Tmiss distribu- events are reweighted as a function of E Tmiss and of the tion in event classes with two photons, which also affects number of selected jets to match the respective distributions the m eff distribution. To correct for this mismodelling a in the data for the inclusive diphoton sample in the range 123 120 Page 12 of 45 Eur. Phys. J. C (2019) 79:120 Events / class 106 105 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets miss 104 ET + single lepton 103 102 10 1 10−1 ET 1μ1j ET 1μ2j ET 1μ3j ET 1μ4j ET 1μ5j ET 1μ6j ET 1μ7j ET 1μ8j ET 1e1j ET 1e2j ET 1e3j ET 1e4j ET 1e5j ET 1e6j ET 1e7j ET 1e8j ET 1μ1b1j ET 1μ1b2j ET 1μ1b3j ET 1μ1b4j ET 1μ1b5j ET 1μ1b6j ET 1μ1b7j ET 1μ2b1j ET 1μ2b2j ET 1μ2b3j ET 1μ2b4j ET 1μ2b5j ET 1μ2b6j ET 1μ3b1j ET 1μ3b2j ET 1μ3b3j ET 1μ3b4j ET 1μ4b1j ET 1μ4b2j ET 1e1b1j ET 1e1b2j ET 1e1b3j ET 1e1b4j ET 1e1b5j ET 1e1b6j ET 1e1b7j ET 1e2b1j ET 1e2b2j ET 1e2b3j ET 1e2b4j ET 1e2b5j ET 1e2b6j ET 1e3b1j ET 1e3b2j ET 1e3b3j ET 1e3b4j ET 1e4b1j ET 1e4b2j ET 1μ1b ET 1μ2b ET 1μ3b ET 1μ4b 1e1b ET 1e2b ET 1e3b ET 1e4b miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss E T Fig. 3 The number of events in data, and for the different SM back- the given event class. The hatched bands indicate the total uncertainty ground predictions considered, for classes with large E Tmiss , one lepton of the SM prediction. This figure shows 60 out of 704 event classes, the and (b-)jets (no photons). The classes are labelled according to the mul- remaining event classes can be found in Figs. 11, 12, 13, 14, 15, 16, 17, tiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed objects for 18, 19, 20, 21, 22, 23 of Appendix C E Tmiss < 100 GeV. In no other event classes was the mis- regions of possible deviations, this analysis uses an algorithm modelling large enough to warrant such a procedure. for multiple hypothesis testing. The algorithm locates a single The application of scale factors also outside the region region of largest deviation for specific observables in each where data to Monte Carlo comparisons are made would be event class. cross-checked in the dedicated reanalysis of any deviation. In the following, an algorithm derived from the algorithm used in Ref. [5] is applied to the 2015 dataset. 3.2.4 Comparison of the event yields with the MC prediction 3.3.1 Choice of variables After classification, 704 event classes are found with at least For each event class, the m eff and m inv distributions are one data event or an SM expectation greater than 0.1 events. considered in the form of histograms. The invariant mass The data and the background predictions from MC simula- is computed from all visible objects in the event, with no tion for these classes are shown in Fig. 3 and Appendix C. attempt to use the E Tmiss information. These variables have Agreement is observed between data and the prediction in been widely used in searches for new physics, and are sen- most of the event classes. In events classes having more than sitive to a large range of possible signals, manifesting either two b-jets and where the SM expectation is dominated by as bumps, deficits or wide excesses. Several other commonly t t¯ production, the nominal SM expectation is systematically used kinematic variables have also been studied for various slightly below the data. Data events are found in 528 out of models, but were not found to significantly increase the sensi- 704 event classes. These include events with up to four lep- tivity. The approach is however not limited to these variables, tons (muons and/or electrons), three photons, twelve jets and as discussed in Sect. 2. eight b-jets. There are 18 event classes with an SM expec- For each histogram, the bin widths h(x) as a function of tation of less than 0.1 events; no more than two data events the abscissa x are determined using: are observed in any of these, and they are not considered further in the analysis. No outstanding event was found in Nobjects  those channels. The remaining 686 classes are retained for h(x) = k 2 σi2 (x/2), statistical analysis. i=1 3.3 Step 3: Sensitive variables and search algorithm where Nobjects is the number of objects in the event class, k is the width of the bin in standard deviations, and σi (x/2) is In order to quantitatively determine the level of agreement the expected detector resolution in the central region for the between the data and the SM expectation, and to identify pT of object i evaluated at pT = x/2 to roughly approx- 123 Eur. Phys. J. C (2019) 79:120 Page 13 of 45 120 imate the largest pT -scale in the event. An exception to transverse momentum (E Tmiss 2μ1 j) and Fig. 4f shows the this is the missing transverse momentum resolution (σ ETmiss), effective mass distribution of the event class with three light which is a function of E T , where E T is approximated jets and large missing transverse momentum (E Tmiss 3 j). The by the effective mass minus the E Tmiss object requirement: regions with the largest deviation found by the search algo- σ ETmiss E T = x − 200 GeV . The E Tmiss object is only con- rithm in these distributions, an excess in Fig. 4a–c, f, and a sidered in the binning of the effective mass histograms. A deficit in Fig. 4d, e, are indicated by vertical dashed lines. ±1σ interval is used for the bin width (k = 2) for all objects To minimize the impact of few MC events, 213, 992 except for photons and electrons, for which a ±3σ interval regions where the background prediction has a total relative is used (k = 6) to avoid having too finely binned histograms uncertainty of over 100% are discarded by the algorithm. Dis- with few MC events. This results in variable bin widths with carding a region forces the algorithm to consider a different values ranging from 20 GeV to about 2000 GeV. For a given or larger region in the event class, or if no region in the event event class, the scan starts at a value of the scanned observable class satisfies the condition, to discard the entire event class.5 larger than two times the sum of the minimum pT require- For all discarded regions with Nobs > 3 a p0 -value is calcu- ment of each contributing object considered (e.g. 100 GeV for lated. If the p0 -value is smaller than the pchannel -value (or if a 2μ class). This minimises spurious deviations which might there is no ROI and hence no pchannel -value), it is evaluated arise from insufficiently well modelled threshold regions. manually by comparing it with the distribution of pchannel - values from the scan. This is done for 27 event classes among 3.3.2 Algorithm to search for deviations of the data from which the smallest p0 -value observed in a discarded region is the expectation 0.01. To model the analysis of discarded regions in pseudo- experiments, regions are allowed to have larger uncertainties The algorithm identifies the single region with the largest if they fulfil the Nobs > 3 criterion. upward or downward deviation in a distribution, provided In addition to monitoring regions discarded due to a total in the form of a histogram, as the region of interest (ROI). uncertainty in excess of 100%, regions discarded due to The total number of independent bins is 36,936, leading to NSM < 0.01 but with Nobs > 3 would also be monitored 518,320 combinations of contiguous bins (regions4 ) with an individually; however, no such region has been observed. SM expectation larger than 0.01 events. For each region with Tables 4 and 5 list the three event classes with the largest an SM expectation larger than 0.01, the statistical estimator deviations in the m inv and m eff scans respectively. The largest p0 is calculated as defined in Eqs. (1)–(3). Here, p0 is to be deviation reported by a dedicated search using the same interpreted as a local p0 -value. The region of largest deviation dataset was observed in an inclusive diphoton data selection found by the algorithm is the region with the smallest p0 - at a diphoton mass of around 750 GeV with a local signifi- value. Such a method is able to find narrow resonances and cance of 3.9σ [49]. Due to the different event selections and single outstanding bins, as well as signals spread over large background estimates the excess has a lower significance in regions of phase space in distributions of any shape. this analysis. The excess was not confirmed in a dedicated To illustrate the operation of the algorithm, six exam- analysis with 2016 data [50]. ple distributions are presented. Figure 4a shows the invari- ant mass distribution of the event class with one photon, 3.4 Step 4: Generation of pseudo-experiments three light jets and large missing transverse momentum (E Tmiss 1γ 3 j), which has the smallest pchannel -value in the As described in Sect. 2.4, pseudo-experiments are generated m inv scan. Figure 4b shows the effective mass distribution of to derive the probability of finding a p0 -value of a given the event class with one muon, one electron, four b-jets and size, for a given observable and algorithm. The pchannel -value two light jets (1μ1e4b2 j), which has the smallest pchannel - distributions of the pseudo-experiments and their statistical value in the m eff scan. Figure 4c shows the invariant mass properties can be compared with the pchannel -value distribu- distribution of the event class with one electron, one photon, tion obtained from data. Correlations in the uncertainties of two b-jets and two light jets (1e1γ 2b2 j). Figure 4d shows the SM expectation affect this probability and their effect is the effective mass distribution of the event class with six light taken into account in the generation of pseudo-data as out- jets (6 j). Figure 4e shows the invariant mass distribution of lined in the following. the event class with two muons, a light jet and large missing For the experimental uncertainties, each of the 35 sources of uncertainty is varied independently by drawing a value at random from a Gaussian pdf. This value is assumed to 4 A histogram of n bins has 1 region of n contiguous bins, 2 regions of be 100% correlated across all bins and event classes. The n −1 contiguous bins, etc. down to n regions of single bins. Therefore, it n has i=1 i = n(n + 1)/2 regions. When combining bins, background uncertainties are conservatively treated as correlated among the bins 5 In the m inv and m eff scan respectively, 72 and 87 event classes are with the exception of MC statistical uncertainties. discarded since they have no ROI. 123 120 Page 14 of 45 Eur. Phys. J. C (2019) 79:120 (a) (b) (c) (d) (e) (f) Fig. 4 Example distributions showing the region of interest (ROI), i.e. f An upward fluctuation in the m eff distribution of the E Tmiss 3 j channel. the region with the smallest p0 -value, between the vertical dashed lines. The hatched band includes all systematic and statistical uncertainties a E Tmiss 1γ 3 j channel, which has the largest deviation in the m inv scan. from MC simulations. In the ratio plots the inner solid uncertainty band b 1μ1e4b2 j channel, which has the largest deviation in the m eff scan. c shows the statistical uncertainty from MC simulations, the middle solid An upward fluctuation in the m inv distribution of the 1e1γ 2b2 j channel. band includes the experimental systematic uncertainty, and the hatched d A downward fluctuation in the m eff distribution of the 6 j channel. e A band includes the theoretical systematic uncertainty downward fluctuation in the m inv distribution of the E Tmiss 2μ1 j channel. 123 Eur. Phys. J. C (2019) 79:120 Page 15 of 45 120 Table 4 List of the three Largest deviations in m inv scan channels with the smallest pchannel -values in the scan of the Channel pchannel (×10−3 ) Nobs NSM ± δ NSM Region (GeV) m inv distributions E Tmiss 1γ 3 j 2.81 9 2.15 ± 0.66 670–732 1μ 1e 4b 2 j 2.91 2 0.042 ± 0.037 1227–1569 1e 1b 4 j 3.44 160 105 ± 14 726–809 Table 5 List of the three Largest deviations in m eff scan channels with the smallest pchannel -values in the scan of the Channel pchannel (×10−3 ) Nobs NSM ± δ NSM Region (GeV) m eff distributions 1μ 1e 4b 2 j 2.66 2 0.040 ± 0.036 992–1227 1μ 1γ 5 j 3.98 4 0.45 ± 0.18 750–895 3b 1 j 4.87 4 0.42 ± 0.24 3401–3923 (a) (b) Fig. 5 A comparison of different correlation assumptions for scale are shown on the left (a), while the fractions in the scan of the m eff distri- variations: 100% correlated; 50% correlated and 50% uncorrelated; and bution having at least three pchannel -values smaller than pmin are shown 100% uncorrelated. The fractions of pseudo-experiments in the scan of on the right (b) the m inv distribution having at least one pchannel -value smaller than pmin uncertainty in the normalization of the various backgrounds Changing the size of the theoretical uncertainties by a is also considered as 100% correlated. Likewise, theoreti- factor of two leads to a change of less than 5% in the cal shape uncertainties, including those estimated from scale − log10 ( pmin ) thresholds at which a dedicated analysis is variations or the differences with alternative generators, are triggered. The correlation assumptions in the theoretical assumed to be 100% correlated, with the exception of the uncertainties were also tested. Figure 5 shows the effect of uncertainties which are used for some SM processes with changing the correlation assumption for all theoretical shape small cross-sections. The latter uncertainties are assumed uncertainties that are nominally taken as 100% correlated. to be uncorrelated, both between event classes and between This test decorrelates the bin-by-bin variations due to the the- bins of the same event class. Scale variations are applied in oretical shape uncertainties in the pseudo-data while retain- the generation of pseudo-experiments by varying the renor- ing the correlation when summing over selected bins in the malization, factorization, resummation and merging scales scan, thus testing the impact of an incorrect assumption in independently. The values for each scale of a given pseudo- the correlation model. By comparing the nominal assump- experiment are 100% correlated between all bins and event tion of 100% correlation with a 50% correlated component, classes. The scales are correlated between processes of the and a fully uncorrelated assumption, the threshold at which same type which are generated with a similar generator set- a dedicated analysis is triggered is changed by a negligible up, i.e. scales are correlated among the W/Z /γ + jets pro- amount. cesses, among all the diboson processes, among the t t¯+W/Z processes, and among the single-top processes. 123 120 Page 16 of 45 Eur. Phys. J. C (2019) 79:120 (a) (b) (c) (d) Fig. 6 The fraction of pseudo-experiments which have at least one, ified background prediction. The scan results of the data tested against two and three pchannel -values below a given pmin , given for both the the modified background prediction are indicated with solid arrows. For pseudo-experiments generated from the nominal SM expectation and reference the scan results under the SM hypothesis are plotted as dashed tested against the nominal expectation (dashed) and for those tested arrows. The largest deviation after removing the W Z process from the against the modified expectation (‘SM, W Z removed’) in which the background expectation is found in the m eff distribution of the 3μ event W Z diboson process is removed (solid). The m inv scan is shown in a class. The distributions of the data and the expectation with both W Z and the m eff scan in b. The horizontal dotted lines show the fractions of included and W Z removed are shown in c and d respectively pseudo-experiments yielding Pexp,i < 5% when tested against the mod- 3.5 Step 5: Evaluation of the sensitivity of the strategy Figure 6 shows how removing the W Z process from the background prediction affects the three smallest expected 3.5.1 Sensitivity to standard model processes pchannel -values. In Fig. 6a, b, the dashed curves show the nominal expected pchannel distribution obtained from pseudo- The sensitivity of the procedure is evaluated with two differ- experiments. These define the pmin thresholds for which ent methods that either use a modified background estimation Pexp,i < 5% and vertical dotted lines are drawn at the through the removal of SM processes or in which signal con- threshold values. The solid lines show the pchannel distribu- tributions are added to the pseudo-data sample. As a figure tions obtained by testing pseudo-experiments generated from of merit, the fraction of ‘signal’ pseudo-experiments with the SM prediction against the modified background predic- Pexp,i < 5% for i = 1, 2, 3 is computed. tion which has the W Z diboson process removed. It can be observed that in this case the m eff scan is more sensitive; the 123 Eur. Phys. J. C (2019) 79:120 Page 17 of 45 120 (a) (b) (c) (d) Fig. 7 The fraction of pseudo-experiments which have at least one, two diction. The scan results of the data tested against the modified back- and three pchannel -values below a given pmin , given for both the pseudo- ground prediction are indicated with solid arrows. For reference the experiments generated from the nominal SM expectation and tested scan results under the SM hypothesis are plotted as dashed arrows. The against the nominal expectation (dashed) and for those tested against largest deviation after removing the t t¯γ process from the background the modified expectation (‘SM, t t¯γ removed’) in which the t t¯γ process expectation is found in the m eff distribution of the 1e1γ 1b2 j event is removed (solid). The m inv scan is shown in a and the m eff scan in b. class. The distributions of the data and the expectation with both t t¯γ The horizontal dotted lines show the fractions of pseudo-experiments included and t t¯γ removed are shown in c and d respectively yielding Pexp,i < 5% when tested against the modified background pre- fraction of ‘signal’ pseudo-experiments with Pexp,i < 5% is d shows the m eff distributions of the data with the full SM about 80% in all three cases i = 1, 2, 3. prediction and the modified prediction respectively. This test Additionally, in Fig. 6a, b, the three smallest pchannel - uses the conclusion from Sect. 3.6 and is performed in retro- values observed in the data are shown by arrows, both when spect. In the case of a significant deviation, this test would be tested against the full SM prediction (dashed) and when performed with pseudo-data to assess the sensitivity of the tested against the modified prediction (solid). For all three search to a missing background. cases (i = 1, 2, 3), Pexp,i < 5% is found again. This means Figure 7 shows the effect of removing the t t¯ + γ process. that a dedicated analysis would be performed for the three Again the m eff scan is slightly more sensitive, and about 70% event classes in which the pchannel -values are observed, i.e. of ‘signal’ pseudo-experiments have Pexp,i < 5% in all three 3μ, 1μ2e1 j, and 2μ1e1 j, likely resulting in the discovery cases i = 1, 2, 3. In the data, Pexp,i < 5% is found again for of an unexpected signal due to W Z production. Figure 6c, all three cases (i = 1, 2, 3). A dedicated analysis would be 123 120 Page 18 of 45 Eur. Phys. J. C (2019) 79:120 (a) (b) Fig. 8 The fraction of pseudo-experiments in which a deviation is or b gluino pairs with g̃ → t t¯χ̃10 decays and various masses. The line found with a pchannel -value smaller than a given pmin . Distributions corresponding to the injection of a Z  boson with a mass of 4 TeV and are shown for pseudo-experiments generated from the SM expectation a gluino with a mass of 1600 GeV overlap with the line obtained from (circular markers), and after injecting signals of a inclusive Z  decays the SM-only pseudo-experiments due to the small signal cross-section performed for the three classes 1μ1γ 2b1 j, 1e1γ 2b2 j, and in Fig. 8 that this search is sensitive to a Z  boson with a mass 1μ1γ 1b3 j, likely resulting in the discovery of an unexpected of about 2.5 TeV as more than 90% of the signal-injected signal due to t t¯ + γ production. pseudo-experiments show a deviation for which Pexp,1 < It is interesting to note that these discoveries would have 5%. Similar sensitivity is expected for a gluino with a mass been made without a priori knowledge of the existence of of about 1 TeV. The probability of discovering a new-physics these processes. signal in a new dataset with a dedicated search in the selected event classes is estimated in the next section. 3.5.2 Sensitivity to new-physics signals 3.5.3 Sensitivity of a second independent dataset Figure 8 shows the sensitivity for the two benchmark signals considered as a function of the mass of the produced parti- In step 7 a dedicated analysis of a deviation is performed on cle. For the Z  model, where the mass of the resonance can an independent dataset. The sensitivity of step 7 is evaluated be reconstructed from its decay products, the sensitivity to with pseudo-experiments. the signal is found to be the largest in the scan of the m inv A first pseudo-experiment emulates the original dataset distribution. Gluinos undergo a cascade decay process to the on which this analysis is performed. The scan algorithm is lightest neutralino, which is undetected and leads to missing applied after which eight different cases can be distinguished transverse momentum. It is not possible to fully reconstruct where Pexp,i is either larger or smaller than 5% for i = 1, 2, 3. an event from gluino pair production due to the presence of In seven cases at least one Pexp,i < 5% and a new inde- neutralinos in the final state. The sensitivity to the gluino sig- pendent pseudo-experiment is generated to emulate a new nal is therefore found to be the largest in the m eff scan, where independent dataset with the same integrated luminosity. The a broad excess at large values of this quantity is expected. one, two or three data selections for which Pexp,i < 5% are Exclusion and discovery sensitivity have to be carefully applied to the second pseudo-experiment to obtain the p0 - distinguished when the results of this search are compared values for these selections. Although the systematic uncer- with model-based searches. An exclusion sensitivity at the tainties may be reduced by applying data-driven estimates of 95% CL in a dedicated search roughly corresponds to a sin- the background, they were assumed to have the same size in gle class having a p0 -value for a discovery test smaller than the second pseudo-experiment to make a conservative esti- 0.05. Consequently, the sensitivity to a benchmark signal cor- mate. The systematic uncertainties are also expected to be responding to a given particle mass should be compared with partially correlated between two datasets but here they were the discovery sensitivity of other searches for a Z  boson or assumed to be uncorrelated. gluino. In four of the seven cases Pexp,1 < 5% and these cases As previously described, a deviation for which Pexp,i < are grouped together into a ‘one signal region’ class. This 5% promotes the selection to a signal region for a dedicated class shows the sensitivity when there would be only a single analysis. By applying this sensitivity criterion, it can be seen data-derived signal region. The case where only Pexp,3 < 5% 123 Eur. Phys. J. C (2019) 79:120 Page 19 of 45 120 (a) (b) Fig. 9 The fraction of cases in which the first pseudo-experiment mass m Z  = 2.5 TeV or b gluino pairs with mass m g̃ = 1.0 TeV has Pexp,i < 5% and triggers a second pseudo-experiment which and g → t t¯χ̃10 decays. The signal region tested in the follow-up yields a value for − nk=1 log10 ( p0 k ) smaller than a given value pseudo-experiment is defined by the preceding pseudo-experiment. The (− nk=1 log10 ( pmin,k )). Here n denotes the minimum number of event 5σ thresholds are obtained by extrapolating the SM-only fractions to classes (1, 2 or 3) for which Pexp,i < 0.05. The distribution is shown 5.7 · 10−7 and are indicated at the top of the figure for n = 1 (left), for pseudo-experiments generated from the SM expectation, and from n = 2 (middle) or n = 3 (right) event classes an SM-plus-signal expectation of a inclusive decays of a Z  boson with is called the ‘three signal region’ class. The two remaining In the case of a 1.0 TeV gluino the sensitivity is about 5σ . cases where Pexp,2 < 5% define the ‘two signal region’ class. The sensitivity increases to about 1.1 TeV if the integrated These classes show cases when a data-derived signal region luminosity of the two datasets combined is increased to about is found only by a combination of two and three regions. 10 fb−1 by doubling the size of the second dataset to 6.4 fb−1 . For each of the  three classes (n = 1, 2, 3) the sta- ATLAS has determined the discovery sensitivity of the ded- tistical estimator nk=1 p0 k is computed. Figure 9 shows, icated searches for gluinos decaying to quarks, a W boson as a function of the estimator in the logarithmic form and a neutralino. This dedicated search estimates a local sig- − nk=1 log10 ( p0 k ) the fraction of cases in which the nificance (i.e. not corrected for trial factors of the dedicated first pseudo-experiment has Pexp,i < 5% and triggers searches) of 5σ with a luminosity of 10 fb−1 for gluinos a second pseudo-experiment which yields a value for with a mass of 1.35 TeV assuming a systematic uncertainty − nk=1 log10 ( p0 k ) above a threshold given by the value on of 25% [51]. the horizontal axis. This is done for pseudo-experiments gen- It should be noted that, with this strategy, these signals erated from the SM expectation (SM-only) and for pseudo- are found without any a priori assumptions about the model, experiments generated from the SM expectation plus Z  or including the mass and the decay chain of the gluinos or the gluino signal contributions. The 5σ lines are derived from the Z  boson. It can therefore be concluded that this procedure fractions given by the SM-only lines, as these correspond to could also be sensitive to possible unexpected signals for new the probability of false positives which defines the level of physics. significance. It should be noted that the SM-only lines with circular markers start at a fraction of 0.05 by construction of 3.6 Step 6: Results the Pexp,i < 5% definition. The n = 2 and n = 3 lines show the gain in sensitivity when a deviation in one or two chan- In step 6 the pchannel -values found in the analysis of the 2015 nels, respectively, is not large enough to define a data-derived ATLAS data are interpreted by comparing them with the signal region. It does not show the gain in sensitivity from pchannel -values found in the pseudo-experiments. considering multiple channels when a single channel defines Figure 10 shows the fractions of pseudo-experiments that a signal region. Signals which produce one or more large have at least one, two or three pchannel -values below a given deviations therefore lower the number of cases in the n = 2 threshold ( pmin ) in the scans of the m inv and the m eff distri- and 3 categories, while signals producing deviations close to butions. The statistical tests in both distributions for the three the Pexp,i < 5% threshold (e.g. for higher Z  masses) would leading pchannel -values are all consistent at the Pexp,i > 50% raise the number of cases in the n = 2 and 3 categories. A level with the SM expectation of pchannel -values obtained Z  boson with a mass of 2.5 TeV would yield a discovery in from pseudo-experiments. Changing the size of the theoreti- almost all cases. cal shape uncertainties by a factor of two leads to a change in 123 120 Page 20 of 45 Eur. Phys. J. C (2019) 79:120 (a) (b) Fig. 10 The fractions of pseudo-experiments (Pexp,i ( pmin )) which coloured arrows represent the three smallest pchannel -values observed have at least one, two or three pchannel -values (circular, square, and in data. Dashed lines are drawn at Pexp,i = 5% and at the pmin -values rhombic markers respectively) smaller than a given threshold ( pmin ) in corresponding to 1σ , 2σ , 3σ , 4σ and 5σ local significances. Details of the scans of a the m inv distributions, and of b the m eff distributions. The the deviations can be found in Tables 4 and 5 the three smallest pchannel -values of a factor of two. It there- physics without an a priori knowledge of the existence of the fore does not lead to an appreciable change in the result. processes. In conclusion, no significant deviations are found in the No significant deviations are found in the 2015 dataset and 2015 dataset and consequently no dedicated analysis using consequently no dedicated analysis using data-derived signal data-derived signal regions (step 7) is initiated. regions is performed. The strategy discussed in this paper will be useful to search for signals of unknown particles and interactions in the subsequent Run 2 datasets. 4 Conclusions Acknowledgements We thank CERN for the very successful oper- ation of the LHC, as well as the support staff from our institutions A strategy for a model-independent general search to find without whom ATLAS could not be operated efficiently. We acknowl- edge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, potential indications of new physics is presented. Events Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, are classified according to their final state into many event Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; classes. For each event class an automated search algorithm CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIEN- tests whether the data is compatible with the Monte Carlo CIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Repub- lic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, simulated Standard Model expectation in several distribu- France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, tions sensitive to the effects of new physics. For each dis- Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, tribution the search algorithm is repeated on many pseudo- Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, experiments to make a frequentist estimate of the statistical Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portu- gal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federa- significance of the three largest deviations. A data selection tion; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slove- in which a significant deviation is observed defines a data- nia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg derived signal region which will be tested on a new dataset Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, in a dedicated analysis with an improved background model. Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups The strategy has been applied to the data collected by the and members have received support from BCKDF, CANARIE, CRC ATLAS experiment at the LHC during 2015, correspond- and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and ing to a total of 3.2 fb−1 of 13 TeV pp collisions. In this Marie Skłodowska-Curie Actions, European Union; Investissements d’ dataset, exclusive event classes containing electrons, muons, Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Ger- many; Herakleitos, Thales and Aristeia programmes co-financed by EU- photons, b-tagged jets, non-b-tagged jets and missing trans- ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA verse momentum have been scanned for deviations from the Programme Generalitat de Catalunya, Spain; The Royal Society and MC-based SM prediction in the distributions of the effective Leverhulme Trust, United Kingdom. The crucial computing support mass and the invariant mass. Sensitivity studies with var- from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Den- ious toy signals (t t¯ + γ , W Z , gluino, and Z  production) mark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), have shown that the strategy could discover signals for new 123 Eur. Phys. J. C (2019) 79:120 Page 21 of 45 120 INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Tai- cally are also generated with Sherpa 2.1.1 including up to wan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and four partons at LO. The W /Z + jets events were normal- large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [52]. ized to their inclusive next-to-next-to-leading-order (NNLO) cross-sections [60,61]. Simulated samples of massive vector Data Availability Statement This manuscript has no associated data bosons produced in association with one or two real photons or the data will not be deposited. [Authors’ comment: “All ATLAS were also generated with Sherpa 2.1.1 with a ME calculated scientific output is published in journals, and preliminary results are made available in Conference Notes. All are openly available, with- at LO for up to three partons. They are scaled to their NLO out restriction on use by external parties beyond copyright law and the cross-sections computed with MCFM [62,63]. standard conditions agreed by CERN. Data associated with journal pub- Samples of prompt photon production in association with lications are also made available: tables and data from plots (e.g. cross jets (γ + jets) were generated using Sherpa 2.1.1. For these section values, likelihood profiles, selection efficiencies, cross section limits, ...) are stored in appropriate repositories such as HEPDATA samples up to four real parton emissions are included at LO. (http://hepdata.cedar.ac.uk/). ATLAS also strives to make additional Events containing two prompt photons (γ γ + jets) were also material related to the paper available that allows a reinterpretation generated with Sherpa 2.1.1. Matrix elements were calcu- of the data in the context of new theoretical models. For example, an lated with up to two partons at LO. The gluon-induced box extended encapsulation of the analysis is often provided for measure- ments in the framework of RIVET (http://rivet.hepforge.org/).” This process is also included. These samples were scaled to data information is taken from the ATLAS Data Access Policy, which is a following the procedure described in Sect. 3.2.3. public document that can be downloaded from http://opendata.cern.ch/ Top-quark pair production events, and single top quarks in record/413 [opendata.cern.ch].] the W t- and s-channels, were simulated using the Powheg- Open Access This article is distributed under the terms of the Creative Box v2 [64] generator with the CT10 PDF set, as detailed Commons Attribution 4.0 International License (http://creativecomm in Ref. [65]. The top quark mass was set to 172.5 GeV. The ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, h damp parameter, which regulates the transverse momentum and reproduction in any medium, provided you give appropriate credit of the first extra emission beyond the Born configuration and to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. thus controls the pT of the t t¯ system, was set to the mass of Funded by SCOAP3 . the top quark. Electroweak t-channel single-top-quark events were generated using the Powheg- Box v1 generator. This generator uses the four-flavour scheme for the NLO matrix- element calculations together with the fixed four-flavour PDF Appendix set CT10f4. For all top-quark processes, top-quark spin corre- lations are preserved (for the single-top t-channel, top quarks A Details of the Monte Carlo samples were decayed using MadSpin [66]). An alternative sample of t t¯ was generated with the Sherpa 2.1.1 generator, including A.1 Monte Carlo programs and settings up to one additional parton at NLO and up to four additional partons at LO accuracy, interfaced to the parton shower using Samples of multijet production were simulated with 2 → the ME+PS@NLO prescription. The parton shower (PS), 2 matrix elements (ME) at leading order (LO) using the fragmentation, and the underlying event of the Powheg- Pythia 8.186 generator [17]. The A14 [25] set of shower Box samples were simulated using Pythia 6.428 [67] with and multiple parton interactions parameters (tune) was used the CTEQ6L1 PDF set and the corresponding Perugia 2012 together with the NNPDF2.3LO PDF set [24]. Alternative tune (P2012) [68]. The t t¯ and single-top-quark events were multijet samples with 2 → 2 ME at LO were generated with normalized to the NNLO cross-section including the resum- Herwig++ 2.7.1 [53] with the UEEE5 underlying-event tune mation of soft gluon emission at next-to-next-to-logarithm and the CTEQ6L1 [33] PDF set, and with Sherpa 2.1.1 [20] accuracy [69–71] using Top++2.0 [72]. Both the default with ME for 2 → 2 and 2 → 3 partons at LO merged and the alternative t t¯ samples were corrected to reproduce using the ME+PSLO prescription. All Sherpa samples use the NNLO prediction [73,74] of the top quark pT and the pT the CT10 [54] PDF set and the Sherpa parton shower [55] of the t t¯ system. The contribution of t t¯ + bb̄ was generated with a dedicated shower tuning developed by the Sherpa separately with Sherpa 2.1.1 at NLO; the calculation was authors. performed in the four-flavour scheme and with the CT10f4 Events containing leptonic decays of a W or Z bosons PDF set. with associated jets (W /Z + jets) were simulated using the Diboson samples were generated with the Sherpa 2.1.1 Sherpa 2.1.1 generator [56]. Matrix elements were calcu- generator, and are described in Ref. [75]. The matrix ele- lated using the Comix [57] and Open-Loops [58] generators. ments contain the W W , W Z and Z Z processes and all other They include up to two partons at NLO and four partons at diagrams with four or six electroweak vertices (such as same- leading order (LO), merged using the ME+PS@NLO pre- electric-charge W boson production in association with two scription [59]. Samples with W and Z decaying hadroni- jets, W ± W ± j j). Fully leptonic triboson processes (W W W , 123 120 Page 22 of 45 Eur. Phys. J. C (2019) 79:120 W W Z , W Z Z and Z Z Z ) with on-shell bosons and up to tainties for W + jets and Z + jets are determined by varying six charged leptons were also simulated using Sherpa 2.1.1. the renormalization, factorization and resummation scales in The ME for the Z Z processes were calculated at NLO for the ME by factors 0.5 and 2, together with a change of the up to one additional parton; final states with two and three merging scale from 20 GeV to 15 GeV or 30 GeV. additional partons were calculated at LO. The W Z and W W For top quark pair or single-top production, processes processes were calculated at NLO with up to three extra par- known to NNLO+NNLL [72] or approximate NNLO [69– tons at LO using the ME+PS@NLO prescription. The W W 71], respectively, the cross-section uncertainty is 7%. The final states were generated without bottom quarks in the hard- modelling uncertainty for t t¯ is determined by comparing the scattering process, to avoid contributions from top-quark- nominal Powheg+Pythia NLO+PS sample with an alterna- mediated processes. The triboson processes were calculated tive sample generated with Sherpa including up to two par- with the same configuration and with up to two extra par- tons at NLO and four at LO accuracy in the ME. The single- tons at LO. The generator cross-sections were used for the top quark uncertainty is estimated by varying the renormal- normalization of these backgrounds. ization and factorization scales, and by changing the h damp Samples of top quark production in association with vec- parameter and the shower tune of the Powheg+Pythia sam- tor bosons [76] (W , Z , γ and W W , including the non- ple. An uncertainty in the interference between the W t and resonant γ ∗ / Z contributions) were generated at LO with t t¯ production is estimated by comparing the nominal W t MG5_aMC@NLO 2.2.2 [32] interfaced to Pythia 8.186, sample, where all doubly resonant NLO W t diagrams are with up to two (t t¯W ), one (t t¯ Z ) or no (t t¯W W , t t¯γ ) extra par- removed, with a sample where the cross-section contribu- tons included in the matrix element. The A14 tune was used tion from Feynman diagrams containing two top quarks is together with the NNPDF2.3LO PDF set. The t t¯γ sample subtracted [80]. uses a fixed QCD renormalization and factorization scale of Diboson cross-sections (W W , W Z and Z Z ) are calcu- 2m t , and the top decay was performed in MG5_aMC@NLO lated at NLO, and a 6% uncertainty, evaluated with the to account for hard photon radiation from the top decay prod- MCFM program [62,63], is applied to their cross-sections. ucts. The same generator was also used to simulate the t Z , Their modelling uncertainty is evaluated analogously to 3-top and 4-top quarks processes. The t t¯W , t t¯ Z , t t¯W W V + jets by varying the scales used to perform the calcu- and 4-top samples were normalized to their NLO cross- lation. For W + γ and Z + γ samples, which are computed sections [32] while the LO cross-section from the generator at LO, a 20% uncertainty in the cross-section is assumed, was used for t Z and 3-top quarks. with a further 20% modelling uncertainty assigned in accord The Higgs boson mass was set to 125 GeV and all SM with the measurement in Ref. [81]. Higgs boson decay modes were considered. The produc- The cross-sections for top-quark pair production in associ- tion of the SM Higgs boson in the gluon–gluon fusion (ggF) ation with one or two vector bosons are calculated at NLO and and vector-boson fusion (VBF) channels was modelled using an uncertainty of 15% is used [32]. Their modelling uncer- the Powheg- Box v2 generator with the CT10 PDF set. It tainty is evaluated from variations of the renormalization and was interfaced to Pythia 8.186 with the CTEQ6L1 PDF set factorization scales, together with a change in the merging and the AZNLO tune [77]. Production of a Higgs boson in scale. For t t¯ + γ , an additional 12% uncertainty in the nor- association with a pair of top quarks was simulated using malization is assumed, while a uniform 30% uncertainty is MG5_aMC@NLO 2.2.2 interfaced to Herwig++ 2.7.1 [78] assigned to the modelling [76]. for showering and hadronization. The UEEE5 underlying- Multijet and γ + jets processes are scaled to data fol- event tune was used together with the CT10 (matrix element) lowing the procedure described in Sect. 3.2.2. Therefore, and CTEQ6L1 (parton shower) PDF sets. Simulated samples no uncertainty is applied to their normalization. For multi- of SM Higgs boson production in association with a W or jets the maximum bin-by-bin difference between the Pythia Z boson were produced with Pythia 8.186, using the A14 8 nominal sample and alternative samples generated with tune and the NNPDF2.3LO PDF set. Events were normalized Sherpa and Herwig++ is considered as a shape uncertainty. to their most accurate cross-sections calculations (typically In addition the standard deviation of the 100 replica sets of NNLO) [79]. the NNPDF2.3LO PDF is used. The modelling uncertainty To avoid double counting, events with a hard photon from for γ + jets is estimated from scale variations with the same final-state radiation were removed from the multijet, t t¯ and methodology as for the V + jets samples. The uncertainty W /Z + jets samples. in the γ γ + jets modelling is instead taken to be 30% from parton-level comparisons of samples with varied scales. A.2 Theoretical uncertainties A conservative uncertainty of 20% [79] is assumed for Higgs production in the ggF, VBF and V H channels. A fur- The inclusive W and Z cross-sections are known at NNLO, ther uncertainty of 20% is assigned as a shape uncertainty. with an uncertainty of about 5% [60,61]. Modelling uncer- 123 Eur. Phys. J. C (2019) 79:120 Page 23 of 45 120 For the subdominant triboson (including V +γ γ ), tt H , 3- verse momenta pT < 50 GeV ( pT < 33 GeV) and of size top, 4-top and t Z production processes a 50% uncertainty is R = 10 GeV/ pT for pT > 50 GeV ( pT > 33 GeV). assigned to the event yields, similarly to Ref. [82]. Uncertain- The efficiency of these criteria increases with the lepton ties associated to PDFs are found to be small in all channels transverse momentum, reaching 95% at 25 GeV and 99% compared to the modelling uncertainties of the MC simula- at 60 GeV, as determined in a control sample of Z decays tions. into leptons selected with a tag-and-probe technique [43,44]. Corrections are applied to the MC samples to match the leptons’ trigger, reconstruction and isolation efficiencies in data. B Details of the object reconstruction Photon candidates are reconstructed from an isolated elec- tromagnetic calorimeter energy deposit and are required to Electron candidates are reconstructed from an isolated elec- satisfy the tight identification criteria described in Refs. [46, tromagnetic calorimeter energy deposit matched to an ID 84]. Furthermore, photons are required to have pT > 25 GeV track and are required to have |η| < 2.47, a transverse and |η| < 2.37, excluding the barrel–endcap calorime- momentum pT > 10 GeV, and to pass a loose likelihood- ter transition in the range 1.37 < |η| < 1.52. Pho- based identification requirement [44,83]. The likelihood tons must further satisfy isolation criteria based on both input variables include measurements of calorimeter shower track and calorimeter information [46]. After correcting for shapes and measurements of track properties from the ID. contributions from pile-up, the energy within a cone of The candidate electrons are selected if the matched tracks R = 0.4 around the cluster barycentre is required to γ γ have a transverse impact parameter significance relative to be less than 2.45 GeV + 0.022 × pT , where pT is the the reconstructed primary vertex of |d0 |/σ (d0 ) < 5. Can- transverse momentum of the photon candidate. The energy didates within the transition region between the barrel and of tracks in a cone of R = 0.2 should be less than endcap electromagnetic calorimeters, 1.37 < |η| < 1.52, γ 0.05 × pT . are removed. Jet candidates are reconstructed with the anti-kt algo- Muon candidates are reconstructed in the region |η| < rithm [38] implemented in the FastJet package [85] with 2.7 from muon spectrometer tracks matched to ID tracks. a radius parameter of R = 0.4, using as input three- The muon candidates are selected if they have a transverse dimensional energy clusters in the calorimeter [86] cali- momentum above 10 GeV and pass the medium identification brated to the electromagnetic scale. The reconstructed jets requirements defined in Ref. [43], based on selections on the are then calibrated to the jet energy scale (JES) derived number of hits in the different ID and muon spectrometer from simulation and in situ corrections based on 13 TeV subsystems, and the significance of the charge to momentum data [42,87]. For all jets the expected average energy contri- ratio q/ p. bution from pile-up clusters is subtracted according to the All candidate leptons (electrons and muons) are used jet area prescription [88]. Quality criteria are imposed to for the object overlap removal, as discussed in Sect. 3.1.3. identify jets arising from non-collision sources or detector Tighter requirements on the lepton candidates are imposed, noise and any event containing such a jet is removed [89]. which are then referred to as ‘signal’ electrons or muons All jet candidates are required to have pT > 20 GeV and and are used further in the analysis, i.e. to establish the accu- |η| < 2.8. racy of the background modelling processes or to classify the Identification of jets containing b-hadrons (b-tagging) events. Signal electrons must satisfy a tight likelihood-based is performed with a multivariate discriminant, MV2c20, identification requirement [44,83]. Signal muons must ful- making use of track impact parameters, the b- and c- fil the requirement of |d0 |/σ (d0 ) < 3. The track associated hadron flight paths inside the jet and reconstructed sec- with a signal lepton must have a longitudinal impact parame- ondary vertices [39,40]. The algorithm working point used ter relative to the reconstructed primary vertex, z 0 , satisfying corresponds to a 77% average efficiency obtained for b- |z 0 · sin θ | < 0.5 mm. Isolation requirements are applied to jets in simulated t t¯ events. The rejection factors for light- both the signal electrons and muons. The calorimeter isola- quark jets, c-quark jets and hadronically decaying τ -leptons tion is computed as the sum of the energies of calorimeter in simulated t t¯ events are approximately 140, 4.5 and energy clusters in a cone of size R = 0.2 around the lep- 10, respectively [40]. Jets with |η| < 2.5 which sat- ton. Track isolation is defined as the scalar sum of the pT isfy this b-tagging requirement are identified as b-jet can- of tracks within a variable-size cone around the lepton, in didates. To compensate for differences between data and a cone of size R = 0.2 (0.3) for electron (muon) trans- MC simulation in the b-tagging efficiencies and mistag rates, correction factors are applied to the simulated sam- ples [40]. 123 120 Page 24 of 45 Eur. Phys. J. C (2019) 79:120 C Event yields for all event classes See Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23. Events / class 108 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top 107 s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 106 photon(s) (normalized to data) 105 104 103 102 10 1 10−1 1γ 1j 1γ 2j 1γ 3j 1γ 4j 1γ 5j 1γ 6j 1γ 7j 1γ 8j 1γ 9j 2γ 1j 2γ 2j 2γ 3j 2γ 4j 2γ 5j 2γ 6j 2γ 7j 3γ 1j 1γ 1b1j 1γ 1b2j 1γ 1b3j 1γ 1b4j 1γ 1b5j 1γ 1b6j 1γ 1b7j 1γ 1b8j 1γ 2b1j 1γ 2b2j 1γ 2b3j 1γ 2b4j 1γ 2b5j 1γ 2b6j 1γ 2b7j 1γ 3b1j 1γ 3b2j 1γ 3b3j 1γ 3b4j 1γ 3b5j 1γ 4b1j 1γ 4b2j 2γ 1b1j 2γ 1b2j 2γ 1b3j 2γ 1b4j 2γ 1b5j 2γ 1b6j 2γ 2b1j 2γ 2b2j 2γ 2b3j 2γ 3b2j 1γ 10j 1γ 1b 1γ 2b 1γ 3b 1γ 4b 2γ 1b 2γ 2b 2γ 3γ Fig. 11 The number of events in data, and for the different SM back- objects for the given event class. In event classes with four or more data ground predictions considered, for classes with at least one photon and events, the γ + jets and γ γ + jets MC samples are scaled to data in (b-)jets (no leptons or E Tmiss ). The classes are labelled according to the single-photon and diphoton event classes respectively. The hatched the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed bands indicate the total uncertainty of the SM prediction Events / class 106 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 105 single electron 104 103 102 10 1 10−1 1e5j 1e6j 1e7j 1e8j 1e9j 1e1b4j 1e1b5j 1e1b6j 1e1b7j 1e1b8j 1e1b9j 1e2b1j 1e2b2j 1e2b3j 1e2b4j 1e2b5j 1e2b6j 1e2b7j 1e2b8j 1e3b1j 1e3b2j 1e3b3j 1e3b4j 1e3b5j 1e3b6j 1e3b7j 1e4b1j 1e4b2j 1e4b3j 1e4b4j 1e4b5j 1e5b1j 1e5b2j 1e2b 1e3b 1e4b 1e5b Fig. 12 The number of events in data, and for the different SM back- multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed objects ground predictions considered, for classes with one electron and (b-)jets for the given event class. The hatched bands indicate the total uncer- (no muons, photons or E Tmiss ). The classes are labelled according to the tainty of the SM prediction 123 Eur. Phys. J. C (2019) 79:120 Page 25 of 45 120 Events / class 107 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 106 single muon 105 104 103 102 10 1 −1 10 1μ1j 1μ2j 1μ3j 1μ4j 1μ5j 1μ6j 1μ7j 1μ8j 1μ9j 1μ1b1j 1μ1b2j 1μ1b3j 1μ1b4j 1μ1b5j 1μ1b6j 1μ1b7j 1μ1b8j 1μ1b9j 1μ2b1j 1μ2b2j 1μ2b3j 1μ2b4j 1μ2b5j 1μ2b6j 1μ2b7j 1μ2b8j 1μ3b1j 1μ3b2j 1μ3b3j 1μ3b4j 1μ3b5j 1μ3b6j 1μ3b7j 1μ4b1j 1μ4b2j 1μ4b3j 1μ4b4j 1μ4b5j 1μ5b1j 1μ5b2j 1μ1b 1μ2b 1μ3b 1μ4b 1μ5b Fig. 13 The number of events in data, and for the different SM back- the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed ground predictions considered, for classes with one muon and (b-)jets objects for the given event class. The hatched bands indicate the total (no electrons, photons or E Tmiss ). The classes are labelled according to uncertainty of the SM prediction 105 Events / class ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top 10 4 s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets muon + electron 103 102 10 1 10−1 1μ1e1j 1μ1e2j 1μ1e3j 1μ1e4j 1μ1e5j 1μ1e6j 1μ1e7j 1μ1e1b1j 1μ1e1b2j 1μ1e1b3j 1μ1e1b4j 1μ1e1b5j 1μ1e1b6j 1μ1e1b7j 1μ1e2b1j 1μ1e2b2j 1μ1e2b3j 1μ1e2b4j 1μ1e2b5j 1μ1e2b6j 1μ1e3b1j 1μ1e3b2j 1μ1e3b3j 1μ1e3b4j 1μ1e4b1j 1μ1e4b2j 1μ1e4b5j 1μ1e 1μ1e1b 1μ1e2b 1μ1e3b 1μ1e4b Fig. 14 The number of events in data, and for the different SM back- to the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed ground predictions considered, for classes with one muon, one electron objects for the given event class. The hatched bands indicate the total and (b-)jets (no photons or E Tmiss ). The classes are labelled according uncertainty of the SM prediction 123 120 Page 26 of 45 Eur. Phys. J. C (2019) 79:120 Events / class 109 108 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 107 dilepton 106 105 104 103 102 10 1 −1 10 2μ1j 2μ2j 2μ3j 2μ4j 2μ5j 2μ6j 2μ7j 2μ8j 2e1j 2e2j 2e3j 2e4j 2e5j 2e6j 2e7j 2e8j 2μ1b1j 2μ1b2j 2μ1b3j 2μ1b4j 2μ1b5j 2μ1b6j 2μ1b7j 2μ2b1j 2μ2b2j 2μ2b3j 2μ2b4j 2μ2b5j 2μ2b6j 2μ3b1j 2μ3b2j 2μ3b3j 2μ4b1j 2μ4b2j 2e1b1j 2e1b2j 2e1b3j 2e1b4j 2e1b5j 2e1b6j 2e1b7j 2e2b1j 2e2b2j 2e2b3j 2e2b4j 2e2b5j 2e3b1j 2e3b2j 2e3b3j 2e3b4j 2e4b1j 2e 2μ1b 2μ2b 2μ3b 2μ4b 2μ 2e1b 2e2b 2e3b 2e4b Fig. 15 The number of events in data, and for the different SM back- ing to the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed ground predictions considered, for classes with two same-flavour lep- objects for the given event class. The hatched bands indicate the total tons and (b-)jets (no photons or E Tmiss ). The classes are labelled accord- uncertainty of the SM prediction Events / class 103 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 3- and 4-lepton 102 10 1 3μ1j 3μ2j 3μ3j 3μ4j 3μ5j 4μ1j 4μ2j 3e1j 3e2j 3e3j 3e4j 1μ2e1j 1μ2e2j 1μ2e3j 1μ2e4j 2μ1e1j 2μ1e2j 2μ1e3j 2μ1e4j 3μ1b1j 3μ1b2j 3μ1b3j 3μ1b4j 3μ2b1j 3μ2b2j 4e1j 2μ2e1j 2μ2e2j 3e1b1j 3e1b2j 3e1b3j 3e2b1j 3e2b2j 1μ2e1b1j 1μ2e1b2j 1μ2e1b3j 1μ2e2b1j 1μ2e2b2j 1μ2e2b5j 2μ1e1b1j 2μ1e1b2j 2μ1e1b3j 2μ1e2b1j 2μ1e2b2j 2μ2e1b1j 3e 1μ2e 2μ1e 3μ1b 3μ2b 4e 1μ3e 2μ2e 3μ 4μ 3e1b 3e2b 1μ2e1b 1μ2e2b 2μ1e1b 2μ1e2b 3μ1e2b 4e1b 2μ2e1b Fig. 16 The number of events in data, and for the different SM back- to the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed ground predictions considered, for classes with three or four leptons objects for the given event class. The hatched bands indicate the total and (b-)jets (no photons or E Tmiss ). The classes are labelled according uncertainty of the SM prediction 123 Eur. Phys. J. C (2019) 79:120 Page 27 of 45 120 Events / class 104 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 103 single lepton + photon(s) 102 10 1 10−1 1e1γ 1j 1e1γ 2j 1e1γ 3j 1e1γ 4j 1e1γ 5j 1e1γ 6j 1e1γ 7j 1e2γ 1j 1e2γ 2j 1e2γ 3j 1μ1γ 1j 1μ1γ 2j 1μ1γ 3j 1μ1γ 4j 1μ1γ 5j 1μ1γ 6j 1μ2γ 1j 1μ2γ 2j 1μ2γ 3j 1e1γ 1b1j 1e1γ 1b2j 1e1γ 1b3j 1e1γ 1b4j 1e1γ 1b5j 1e1γ 2b1j 1e1γ 2b2j 1e1γ 2b3j 1e1γ 2b4j 1e1γ 2b5j 1e1γ 3b1j 1e1γ 3b2j 1e1γ 3b3j 1e2γ 1b1j 1e2γ 1b2j 1μ1γ 1b1j 1μ1γ 1b2j 1μ1γ 1b3j 1μ1γ 1b4j 1μ1γ 1b5j 1μ1γ 2b1j 1μ1γ 2b2j 1μ1γ 2b3j 1μ1γ 2b4j 1μ1γ 3b1j 1μ1γ 3b2j 1μ2γ 1b2j 1e1γ 1b 1e1γ 2b 1e1γ 3b 1μ1γ 1b 1μ1γ 2b 1μ1γ 3b 1e1γ 1e2γ 1μ1γ 1μ2γ Fig. 17 The number of events in data, and for the different SM back- the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed ground predictions considered, for classes with one lepton, at least one objects for the given event class. The hatched bands indicate the total photon and (b-)jets (no E Tmiss ). The classes are labelled according to uncertainty of the SM prediction Events / class ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top 3 s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 10 multilepton + photon(s) 102 10 1 2e1γ 1j 2e1γ 2j 2e1γ 3j 2e1γ 4j 2e1γ 5j 1μ1e1γ 1j 1μ1e1γ 2j 1μ1e1γ 3j 2μ1γ 1j 2μ1γ 2j 2μ1γ 3j 2μ1γ 4j 2μ2γ 1j 2μ2γ 2j 2μ1e1γ 1j 2e1γ 1b1j 2e1γ 1b2j 2e1γ 1b3j 2e1γ 2b1j 2e1γ 2b2j 1μ1e1γ 1b1j 1μ1e1γ 1b2j 1μ1e1γ 1b3j 1μ1e1γ 1b4j 1μ1e1γ 2b1j 1μ1e1γ 2b2j 2μ1γ 1b1j 2μ1γ 1b2j 2μ1γ 1b3j 2μ1γ 2b1j 2μ1γ 2b2j 2e1γ 1b 2e1γ 2b 2e1γ 3b 1μ1e1γ 1b 1μ1e1γ 2b 2μ1γ 1b 2μ1γ 2b 3μ1e 2e1γ 2e2γ 3e1γ 1μ1e1γ 2μ1γ 2μ2γ 2μ1e1γ 3μ1γ Fig. 18 The number of events in data, and for the different SM back- ing to the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed ground predictions considered, for classes with at least two leptons, at objects for the given event class. The hatched bands indicate the total least one photon and (b-)jets (no E Tmiss ). The classes are labelled accord- uncertainty of the SM prediction 123 Events / class Events / class 10 10 102 3 102 103 104 105 6 107 10 10 10−1 1 1 123 miss ET 1γ miss miss ET T ET 1γ 1j ET 1j miss miss Emiss ET 1γ 2j miss miss ET 2j 120 Page 28 of 45 ET 1γ 3j miss miss ET 1γ 4j ET 3j miss ET 1γ 5j + jets miss miss ET 1γ 6j ET 4j miss ATLAS ATLAS ET 1γ 1b miss miss ET 5j ET 1γ 1b1j miss miss ET 1γ 1b2j miss ET 6j s = 13 TeV, 3.2 fb s = 13 TeV, 3.2 fb ET 1γ 1b3j miss miss -1 -1 ET 1γ 1b4j ET 7j miss ET 1γ 1b5j miss miss ET 8j ET 1γ 2b miss miss ET 1γ 2b1j ET 9j + photon(s) (+ lepton) miss ET 1γ 2b2j miss miss ET 1γ 2b3j ET 1b miss ET 1γ 2b4j miss miss ET 1b1j ET 1γ 3b miss miss ET 1γ 3b1j ET 1b2j miss ET 1γ 3b2j miss miss ET 1b3j ET 2γ miss miss ET 2γ 1j ET 1b4j miss ET 2γ 2j miss miss ET 2γ 1b1j ET 1b5j miss ET 2γ 1b2j miss miss ET 1b6j ET 1e1γ miss miss ET 1e1γ 1j miss ET 1b7j Data 2015 Data 2015 di-/triboson di-/triboson photon, leptons and (b-)jets. The classes are labelled according to the ground predictions considered, for classes with large E Tmiss , at least one Fig. 20 The number of events in data, and for the different SM back- Fig. 19 The number of events in data, and for the different SM back- (no leptons or photons). The classes are labelled according to the mul- ground predictions considered, for classes with large E Tmiss and (b-)jets ET 1e1γ 2j miss miss ET 1b8j ET 1e1γ 3j miss ET 1e1γ 4j miss miss ET 2b ET 1e1γ 1b miss miss ET 1e1γ 1b1j ET 2b1j miss ET 1e1γ 1b2j miss miss ET 2b2j ET 1e1γ 1b3j 3-/4-top 3-/4-top miss miss Z/W+γ (γ ) Z/W+γ (γ ) ET 1e1γ 1b4j ET 2b3j miss ET 1e1γ 2b miss miss ET 1e1γ 2b1j ET 2b4j miss ET 1e1γ 2b2j miss miss ET 2b5j ET 1e1γ 2b3j miss miss ET 1e2γ 1j ET 2b6j Higgs Higgs miss t t +jets t t +jets of the SM prediction ET 2e1γ miss miss ET 2e1γ 1b ET 2b7j miss ET 1μ1γ miss miss ET 2b8j tainty of the SM prediction ET 1μ1γ 1j miss miss ET 1μ1γ 2j ET 3b miss ET 1μ1γ 3j miss miss γ (γ )+jets γ (γ )+jets ET 1μ1γ 4j ET 3b1j miss miss t t +Z/W/WW t t +Z/W/WW ET 1μ1γ 5j ET 3b2j miss ET 1μ1γ 1b miss miss ET 1μ1γ 1b1j ET 3b3j miss ET 1μ1γ 1b2j miss miss ET 3b4j ET 1μ1γ 1b3j miss miss t t +γ t t +γ ET 1μ1γ 1b4j ET 3b5j miss ET 1μ1γ 2b miss miss Z/W+jets Z/W+jets ET 1μ1γ 2b1j ET 4b miss ET 1μ1γ 2b2j miss miss ET 4b1j ET 1μ1γ 2b3j miss miss ET 1μ1e1γ miss ET 4b2j ET 1μ1e1γ 1j miss miss ET 4b3j ET 1μ1e1γ 2j miss ET 1μ1e1γ 1b miss multijets multijets miss ET 4b4j single top single top ET 1μ1e1γ 1b1j miss miss ET 2μ1γ 1j miss ET 5b1j ET 2μ1γ 2j for the given event class. The hatched bands indicate the total uncer- the given event class. The hatched bands indicate the total uncertainty tiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed objects for Eur. Phys. J. C (2019) 79:120 multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed objects Eur. Phys. J. C (2019) 79:120 Page 29 of 45 120 Events / class 103 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 102 Emiss T + muon + electron 10 1 ET 1μ1e1j ET 1μ1e2j ET 1μ1e3j ET 1μ1e4j ET 1μ1e5j ET 1μ1e6j ET 1μ1e1b1j ET 1μ1e1b2j ET 1μ1e1b3j ET 1μ1e1b4j ET 1μ1e1b5j ET 1μ1e2b1j ET 1μ1e2b2j ET 1μ1e2b3j ET 1μ1e2b4j ET 1μ1e3b1j ET 1μ1e3b2j ET 1μ1e ET 1μ1e1b ET 1μ1e2b ET 1μ1e3b ET 1μ1e4b miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss Fig. 21 The number of events in data, and for the different SM back- ing to the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of the reconstructed ground predictions considered, for classes with large E Tmiss , one muon, objects for the given event class. The hatched bands indicate the total one electron and (b-)jets (no photons). The classes are labelled accord- uncertainty of the SM prediction Events / class ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 102 miss ET + multilepton 10 1 ET 2μ1j ET 2μ2j ET 2μ3j ET 2μ4j ET 2μ5j ET 3μ1j ET 3μ2j ET 2e1j ET 2e2j ET 2e3j ET 2e4j ET 2e5j ET 3e1j ET 3e2j ET 1μ2e1j ET 1μ2e2j ET 1μ2e3j ET 2μ1b1j ET 2μ1b2j ET 2μ1b3j ET 2μ1b4j ET 2μ1b5j ET 2μ2b1j ET 2μ2b2j ET 2μ2b3j ET 2μ2b4j ET 2μ3b1j ET 2μ3b2j ET 2μ1e1j ET 2μ1e2j ET 2μ1e3j ET 3μ1b1j ET 2e1b1j ET 2e1b2j ET 2e1b3j ET 2e1b4j ET 2e2b1j ET 2e2b2j ET 2e2b3j ET 2e3b1j ET 2e ET 3e ET 1μ2e ET 2μ1b ET 2μ2b ET 2μ3b ET 2μ1e ET 2μ ET 2e1b ET 2e2b ET 2e3b ET 1μ2e1b miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss miss Fig. 22 The number of events in data, and for the different SM back- labelled according to the multiplicity and type (e, μ, γ , j, b, E Tmiss ) of ground predictions considered, for classes with large E Tmiss , at least one the reconstructed objects for the given event class. The hatched bands pair of same flavour leptons and (b-)jets (no photons). The classes are indicate the total uncertainty of the SM prediction 123 120 Page 30 of 45 Eur. Phys. J. C (2019) 79:120 Events / class 109 108 ATLAS Data 2015 3-/4-top Higgs t t +Z/W/WW t t +γ single top s = 13 TeV, 3.2 fb -1 di-/triboson Z/W+γ (γ ) t t +jets γ (γ )+jets Z/W+jets multijets 107 dijet and multijet (normalized to data) 106 105 104 103 102 10 1 −1 10 2j 3j 4j 5j 6j 7j 8j 9j 1b10j 1b11j 2b10j 1b1j 1b2j 1b3j 1b4j 1b5j 1b6j 1b7j 1b8j 1b9j 2b1j 2b2j 2b3j 2b4j 2b5j 2b6j 2b7j 2b8j 2b9j 3b1j 3b2j 3b3j 3b4j 3b5j 3b6j 3b7j 3b8j 3b9j 4b1j 4b2j 4b3j 4b4j 4b5j 4b6j 5b1j 5b2j 5b3j 5b4j 5b7j 6b1j 6b2j 6b3j 8b1j 10j 11j 12j 13j 14j 2b 3b 4b 5b 6b 7b Fig. 23 The number of events in data, and for the different SM back- event class. In event classes with four or more data events, the multi- ground predictions considered, for classes with (b-)jets (no E Tmiss , lep- jet MC sample is scaled to data. 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Zhou15d , M. Zhou152 , N. Zhou58c , Y. Zhou7 , C. G. Zhu58b , H. Zhu15a , J. Zhu103 , Y. Zhu58a , X. Zhuang15a , K. Zhukov108 , V. Zhulanov120a,120b , A. Zibell175 , D. Zieminska63 , N. I. Zimine77 , S. Zimmermann50 , Z. Zinonos113 , M. Zinser97 , M. Ziolkowski148 , G. Zobernig179 , A. Zoccoli23a,23b , T. G. Zorbas146 , R. Zou36 , M. Zur Nedden19 , L. Zwalinski35 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany, NY, USA 123 120 Page 40 of 45 Eur. Phys. J. C (2019) 79:120 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) Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey; (b) Faculty of Engineering and Natural Sciences, Istanbul Bilgi University, 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) 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) Physics Department, National Institute for Research and Development of Isotopic and Molecular Technologies, 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, 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 123 Eur. Phys. J. C (2019) 79:120 Page 41 of 45 120 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 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, NT, 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 123 120 Page 42 of 45 Eur. Phys. J. C (2019) 79:120 79 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 80 Graduate School of Science, Kobe University, Kobe, Japan 81 (a) Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 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 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, University of Amsterdam, Amsterdam, The Netherlands 119 Department of Physics, Northern Illinois University, De Kalb, IL, USA 120 (a) Budker Institute of Nuclear Physics, SB RAS, Novosibirsk, Russia; (b) Novosibirsk State University, Novosibirsk, Russia 121 Department of Physics, New York University, New York, NY, USA 122 Ohio State University, Columbus, OH, USA 123 Faculty of Science, Okayama University, Okayama, Japan 124 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK, USA 125 Department of Physics, Oklahoma State University, Stillwater, OK, USA 126 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 123 Eur. Phys. J. C (2019) 79:120 Page 43 of 45 120 127 Center for High Energy Physics, University of Oregon, Eugene, OR, USA 128 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France 129 Graduate School of Science, Osaka University, Osaka, Japan 130 Department of Physics, University of Oslo, Oslo, Norway 131 Department of Physics, Oxford University, Oxford, UK 132 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France 133 Department of Physics, University of Pennsylvania, Philadelphia, PA, USA 134 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 135 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA 136 (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 137 Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic 138 Czech Technical University in Prague, Prague, Czech Republic 139 Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic 140 State Research Center Institute for High Energy Physics, NRC KI, Protvino, Russia 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 Academia Sinica Grid Computing, Institute of Physics, Academia Sinica, Taipei, Taiwan 157 (a) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia; (b) High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 158 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 159 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 160 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 161 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 162 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 163 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 164 Tomsk State University, Tomsk, Russia 165 Department of Physics, University of Toronto, Toronto, ON, Canada 166 (a) TRIUMF, Vancouver, BC, Canada; (b) Department of Physics and Astronomy, York University, Toronto, ON, Canada 167 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 168 Department of Physics and Astronomy, Tufts University, Medford, MA, USA 169 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA 170 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 171 Department of Physics, University of Illinois, Urbana, IL, USA 123 120 Page 44 of 45 Eur. Phys. J. C (2019) 79:120 172 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia, CSIC, Valencia, Spain 173 Department of Physics, University of British Columbia, Vancouver, BC, Canada 174 Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada 175 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 176 Department of Physics, University of Warwick, Coventry, UK 177 Waseda University, Tokyo, Japan 178 Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 179 Department of Physics, University of Wisconsin, Madison, WI, USA 180 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 181 Department of Physics, Yale University, New Haven, CT, USA 182 Yerevan Physics Institute, Yerevan, Armenia a Also at Department of Physics, University of Malaya, Kuala Lumpur, Malaysia b Also at Borough of Manhattan Community College, City University of New York, NY, USA c Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa d Also at CERN, Geneva, Switzerland e Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France f Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland g Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain h Also at Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada (Spain), Spain i Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates j Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece k Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY, USA l Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, UK m Also at Department of Physics, California State University, Fresno, CA, USA n Also at Department of Physics, California State University, Sacramento, CA, USA o Also at Department of Physics, King’s College London, London, UK p Also at Department of Physics, Nanjing University, Nanjing, China q Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia r Also at Department of Physics, University of Fribourg, Fribourg, Switzerland s Also at Department of Physics, University of Michigan, Ann Arbor, MI, USA t Also at Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy u Also at Giresun University, Faculty of Engineering, Giresun, Turkey v Also at Graduate School of Science, Osaka University, Osaka, Japan w Also at Hellenic Open University, Patras, Greece x Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania y Also at II Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany z Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain aa Also at Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, 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 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France aj Also at Louisiana Tech University, Ruston, LA, USA ak Also at Manhattan College, New York, NY, USA al Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia am Also at National Research Nuclear University MEPhI, Moscow, Russia 123 Eur. Phys. J. C (2019) 79:120 Page 45 of 45 120 an Also at Near East University, Nicosia, North Cyprus, Mersin, Turkey ao Also at Ochadai Academic Production, Ochanomizu University, Tokyo, Japan ap Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany aq Also at School of Physics, Sun Yat-sen University, Guangzhou, China ar Also at The City College of New York, New York, NY, USA as Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China at Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia au Also at TRIUMF, Vancouver, BC, Canada av Also at Universita di Napoli Parthenope, Naples, Italy ∗ Deceased 123