Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton-proton collision data

Journal of Instrumentation OPEN ACCESS Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton-proton collision data To cite this article: G. Aad et al 2019 JINST 14 P12006 View the article online for updates and enhancements. This content was downloaded from IP address 150.214.205.64 on 13/02/2020 at 09:39 2019 JINST 14 P12006 Published by IOP Publishing for Sissa Medialab Received:August 2, 2019 Accepted:October 16, 2019 Published:December 10, 2019 Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton-proton collision data The ATLAS collaboration E-mail: [email protected] Abstract: This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81fb−1of proton-proton collision data collected at √s=13TeV between 2015 and 2017. Keywords: Particle identification methods; Performance of High Energy Physics Detectors ArXiv ePrint:1908.00005 c 2019 CERN for the benefit of the ATLAS collaboration. Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. https://doi.org/10.1088/1748-0221/14/12/P12006 2019 JINST 14 P12006 Contents 1 Introduction 1 2 ATLAS detector 2 3 Collision data and simulation samples 3 3.1 Dataset 3 3.2 Simulation samples 4 4 Electron and photon reconstruction 5 4.1 Topo-cluster reconstruction 6 4.2 Track reconstruction, track-cluster matching, and photon conversion reconstruction 8 4.3 Supercluster reconstruction 10 4.4 Creation of electrons and photons for analysis 11 4.5 Performance 12 5 Electron and photon energy calibration 15 5.1 Energy scale and resolution measurements with Z→ee decays 18 5.2 Systematic uncertainties 19 5.3 Validation of the photon energy scale with Z→``γ decays 20 5.4 Energy scale and resolution corrections in low-pile-up data 21 6 Electron identification 23 6.1 Variables in the electron identification 23 6.2 Likelihood discriminant 25 6.3 Efficiency of the electron identification 27 7 Photon identification 27 7.1 Optimization of the photon identification 27 7.2 Efficiency of the photon identification 29 8 Electron and photon isolation 34 8.1 Electron isolation criteria and efficiency measurements 35 8.2 Photon isolation criteria and efficiency measurements 36 8.2.1 Measurement of photon isolation efficiency with radiative Zdecays 38 8.2.2 Photon calorimeter isolation efficiency measurement with inclusive-photon events 39 8.2.3 Photon track-based isolation efficiency measurement with inclusive-photon events 43 8.2.4 Combination of photon isolation scale factors 43 –i– 2019 JINST 14 P12006 9 Electron charge misidentification 43 9.1 Suppression of electron charge misidentification 45 9.2 Measurement of the probability for charge misidentification 45 10 Conclusions 47 The ATLAS collaboration 52 1 Introduction With an integrated luminosity of about 147fb−1, the proton-proton (pp) collision dataset collected by the ATLAS detector between 2015 and 2018 at a centre-of-mass energy of √s=13 TeV will allow significant advances in the exploration of the electroweak scale. Optimal performance in the measurement of electrons and photons plays a fundamental role in searches for new particles, in the measurement of Standard Model cross-sections, and in the precise measurement of the properties of fundamental particles such as the Higgs and Wbosons and the top quark. The ATLAS collaboration published three papers describing the performance of the reconstruction, identification and energy measurement of electrons and photons with 36fb−1of pp collision data collected in 2015 and 2016 [1–3]. New algorithms for electron and photon reconstruction were introduced in 2017. The present paper describes the performance of these algorithms, and extends the analysis to the dataset collected between 2015 and 2017, which corresponds to an integrated luminosity of about 81 fb−1. The discussion is limited to electrons and photons reconstructed in the central calorimeters, covering the pseudorapidity range |η|<2.5. The transition from the reconstruction of electrons and photons based on fixed-size clusters of calorimeter cells towards a dynamical, topological cell clustering algorithm [4] represents the most important modification. The algorithms used for the identification of the candidates and the estimation of their energy have been updated accordingly. The performance of these changes is discussed in detail. In addition, methods allowing an improved rejection of misreconstructed or non-isolated candidates are presented, and are of particular importance for measurements of processes with low cross-sections or high backgrounds, such as the associated production of a Higgs boson with a top-quark pair, or vector-boson scattering at high energy. After a summary of the experimental apparatus and the samples used for this analysis in sections 2and 3, section 4describes the new reconstruction of clusters of energy deposits in the electromagnetic (EM) calorimeter, the estimation of their energy, and the use of information from the inner tracking detector to distinguish between electrons and photons. Section 5summarizes the energy calibration corrections and the associated systematic uncertainties. Sections 6and 7 present the re-optimized electron and photon identification algorithms. Section 8discusses the discriminationbetweenpromptelectronsandphotons andbackgroundsfrom hadrondecays. Finally, studies dedicated to the electron and positron charge identification are reported in section 9. –1– 2019 JINST 14 P12006 2 ATLAS detector The ATLAS experiment [5–7] is a general-purpose particle physics detector with a forwardbackward symmetric cylindrical geometry and almost 4πcoverage in solid angle.1The inner tracking detector (ID) covers the pseudorapidity range |η|<2.5and consists of a silicon pixel detector, a silicon microstrip detector (SCT), and a transition radiation tracker (TRT) in the range |η|<2.0. The TRT provides electron identification capability through the detection of transition radiation photons. It consists of small-radius drift tubes (‘straws’) interleaved with a polymer material creating transition radiation for particles with a large Lorentz factor. This radiation is absorbed by the Xe-based gas mixture filling the straws, discriminating electrons from hadrons over a wide energy range. Due to gas leaks, some TRT modules are filled with an Ar-based gas mixture. The ID is surrounded by a superconducting solenoid producing a 2T magnetic field and provides accurate reconstruction of tracks from the primary pp collision region. It also identifies tracks from secondary vertices, permitting an efficient reconstruction of photon conversions in the ID up to a radius of about 800mm. The EM calorimeter is a lead/liquid-argon (LAr) sampling calorimeter with an accordion geometry. It is divided into a barrel section (EMB) covering the pseudorapidity region |η|<1.475,2 and two endcap sections (EMEC) covering 1.375 <|η|<3.2. The barrel and endcap calorimeters are immersed in three LAr-filled cryostats, and are segmented into three layers for |η|<2.5. The first layer, covering |η|<1.4and 1.5<|η|<2.4, has a thickness of about 4.4 radiation lengths (X0) and is finely segmented in the ηdirection, typically 0.003 ×0.1in ∆η×∆φin the EMB, to provide an event-by-event discrimination between single-photon showers and overlapping showers from the decays of neutral hadrons. The second layer (L2), which collects most of the energy deposited in the calorimeter by photon and electron showers, has a thickness of about 17X0and a granularity of 0.025 ×0.025 in ∆η×∆φ. A third layer, which has a granularity of 0.05 ×0.025 in ∆η×∆φand a depth of about 2X0, is used to correct for leakage beyond the EM calorimeter for high-energy showers. In front of the accordion calorimeter, a thin presampler layer (PS), covering the pseudorapidity interval |η|<1.8, is used to correct for energy loss upstream of the calorimeter. The PS consists of an active LAr layer with a thickness of 1.1cm (0.5 cm) in the barrel (endcap) and has a granularity of ∆η×∆φ=0.025 ×0.1. The transition region between the EMB and the EMEC, 1.37 <|η|<1.52, has a large amount of material in front of the first active calorimeter layer ranging from 5 to almost 10X0. This section is instrumented with scintillators located between the barrel and endcap cryostats, and extending up to |η|=1.6. The hadronic calorimeter, surrounding the EM calorimeter, consists of an iron/scintillator tile calorimeter in the range |η|<1.7and two copper/LAr calorimeters spanning 1.5<|η|<3.2. The acceptance is extended by two copper/LAr and tungsten/LAr forward calorimeters extending up to |η|=4.9, and hosted in the same cryostats as the EMEC. Electron reconstruction in the forward calorimeters is not discussed in this paper. 1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upward. Cylindrical coordinates (r, φ)are used in the transverse plane, φbeing the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θas η=−ln tan(θ/2). The angular distance ∆Ris defined as ∆R≡q(∆η)2+(∆φ)2. The transverse energy is ET=E/cosh(η). 2The EMB is split into two half-barrel modules, which cover the positive and negative ηregions. –2– 2019 JINST 14 P12006 The muon spectrometer, located beyond the calorimeters, consists of three large air-core superconducting toroid systems with eight coils each, with precision tracking chambers providing accurate muon tracking for |η|<2.7and fast-triggering detectors up to |η|=2.4. A two-level trigger system [8] is used to select events. The first-level trigger is implemented in hardware and uses a subset of the detector information to reduce the accepted rate to a maximum of about 100kHz. This is followed by a software-based trigger that reduces the accepted event rate to 1kHz on average, depending on the data-taking conditions. 3 Collision data and simulation samples 3.1 Dataset The analyses described in this paper use the full pp collision dataset recorded by ATLAS between 2015 and 2017 with the LHC operating at a centre-of-mass energy of √s= 13TeV and a bunch spacing of 25ns. The dataset is divided into two subsamples according to the typical mean number of interactions per bunch crossing, hµi, with which it was recorded: •The ‘low-µ’ sample was recorded in 2017 with hµi∼2; after application of data-quality requirements, the integrated luminosity amounts to 147pb−1. •The ‘high-µ’ sample corresponds to an integrated luminosity of 80.5 fb−1; for this sample, hµi was on average 13, 25 and 38 for 2015, 2016 and 2017 data, respectively. The corresponding integrated luminosities are 3.2 fb−1, 33.0 fb−1and 44.3 fb−1. In 2016, a small sample corresponding to 0.7 fb−1of data was recorded without magnetic field in the muon system; it is added to the ‘high-µ’ sample for electron reconstruction and identification studies. Two different LHC filling schemes were used in 2017. The nominal filling scheme, labelled 48b in the following, corresponding to an integrated luminosity of 17.9fb−1and hµi∼32, was built from ‘sub-trains’ of 48 filled bunches followed by seven empty bunches. Simulated event samples use this configuration,3as it represents about 70% of the collected data; the implications of this approximation for the energy calibration are discussed in section 5. The second scheme, labelled 8b4e, corresponding to an integrated luminosity of 26.4 fb−1and hµi∼42, was made of sub-trains of eight filled bunches followed by four empty bunches. To sustain these conditions, a levelling of the instantaneous luminosity at 2×1034 cm−2s−1was necessary at the beginning of the fill, resulting in a peak hµiaround 60. The noise induced by pile-up, or multiple pp interactions occurring in the same bunch crossing as the event of interest or in nearby crossings, is 10% smaller than for the standard configuration for a given µ. The LHC filling scheme for the ‘low-µ’ data sample was 8b4e. Several levels of object identification and isolation criteria are employed to select the event samplesusedin theanalyses describedinthispaper. Electrons areidentifiedusing alikelihood-based methodcombining informationfrom the EMcalorimeter andthe ID. Differentidentification working points, Loose, Medium and Tight are defined [2]. Similar levels are used at trigger level (online), with slightly different inputs. A Very Loose working point is also defined for the online selection. Photons are selected using a set of cuts on calorimeter variables [1] in the pseudorapidity range 3The simulation used in conjunction with 2015 and 2016 data has a similar bunch configuration, consisting of 72 filled bunches followed by eight empty bunches. –3– 2019 JINST 14 P12006 |η|<2.37, withthetransitionregion between thebarreland endcapcalorimeters, 1.37 <|η|<1.52, excluded. Two levels of identification, Loose and Tight, are considered. A Loose identification is used at trigger level to select a sample of inclusive photons. The measurements of the electromagnetic energy response and of the electron identification efficiency use a large sample of Z→ee events selected with single-electron and dielectron triggers. The dielectron high-level triggers use a transverse energy (ET) threshold ranging from 12GeV (2015) to 17 or 24GeV (2016 and 2017) and a Loose (2015) or Very Loose (2016 and 2017) identification criterion. The single-electron high-level trigger has an ETthreshold ranging from 24GeV in 2015 and most of 2016 to 26GeV at the end of 2016 and during 2017; it requires a Tight identification and loose tracking-based isolation criteria. The offline selection for the energy calibration measurement requires two electrons with Medium identification and loose isolation [2] with ET>27 GeV, resulting in ∼36 million Z→ee candidate events. A sample of J/ψ→ee events with at least two electron candidates with ET>4.5GeV and |η|<2.47 was collected for studies with low-ETelectrons using dedicated prescaled dielectron triggers with electron ETthresholds ranging from 4 to 14 GeV. Each of these triggers requires Tight trigger identification and ETabove a certain threshold for one trigger object, while only demanding the electromagnetic cluster ETto be higher than some other (lower) threshold for the second object. Samples of Z→``γ events, used to validate the photon energy scale and measure photon identification and isolation efficiencies at low ET, were selected with the same triggers as for the Z→ee sample for the electron channel and single-muon or dimuon triggers in the muon channel. The dimuon (single-muon) trigger transverse momentum (pT) threshold was 14 (26) GeV at the high-level trigger; a loose tracking-based isolation criterion was applied at the high-level trigger for the single-muon trigger. The µµγ (eeγ) samples, after requiring two muons (electrons) with Medium identification [9], pT>15 GeV (18 GeV) and one tightly identified and loosely isolated photon with ET>15 GeV, contain ∼110000 (∼54000) events. Single-photon triggers with Loose identification and large prescale factors are used for measurements of the photon identification and isolation efficiencies. The lowest transverse energy threshold of these triggers is 10GeV. 3.2 Simulation samples Large Monte Carlo (MC) samples of Z→`` events (`=e, µ) were simulated at next-to-leading order (NLO) in QCD using Powheg [10] interfaced to the Pythia8 [11] parton shower model. The CT10 [12] parton distribution function (PDF) set was used in the matrix element. The AZNLO set of tuned parameters [13] was used, with PDF set CTEQ6L1 [14], for the modelling of nonperturbative effects. Photos++ 3.52 [15] was used for QED emissions from electroweak vertices and charged leptons. To model the background in photon identification and isolation measurements using radiative Zdecays, samples of Z→`` events with up to two additional partons at NLO in QCD and four additional partons at leading order (LO) in QCD were simulated with Sherpa [16] version 2.2.1, using the NNPDF30NNLO [17] PDF in conjunction with the dedicated parton shower tuning developed by the Sherpa authors. Both non-prompt (originating from b-hadron decays) and prompt (not originating from bhadron decays) J/ψ→ee samples were generated using Pythia8. The A14 set of tuned parameters [18] was used together with the CTEQ6L1 PDF set. –4– 2019 JINST 14 P12006 Samples of Z→``γ events with transverse energy of the photon above 10 GeV were generated with Sherpa version 2.1.1 using QCD leading-order matrix elements with up to three additional partons in the final state. The CT10 PDF set was used. Samples of inclusive photon production were generated using Pythia8. The signal includes LO photon-plus-jet events from the hard subprocesses qg→qγand qq →gγ, and photon production from quark fragmentation in LO QCD dijet events. The fragmentation component was modelled by QED radiation arising from calculations of all 2 →2 QCD processes involving light partons (gluons and up, down and strange quarks). A large sample of backgrounds to prompt photon and electron production was generated with Pythia8, including all tree-level 2 →2 QCD processes as well as top-quark pair and weak vectorboson production, filtered at particle level to mimic a first-level EM trigger requirement. For this sample and the inclusive-photon samples, the A14 set of tuned parameters was used together with the NNPDF23LO PDF set [19]. The Pythia8 sample production used the EvtGen 1.2.0 program [20] to model band c-hadron decays. The generated events were processed through the full ATLAS detector simulation [21] based on Geant4 [22]. The MC events were simulated with additional interactions in the same or neighbouring bunch crossings to match the pile-up conditions during LHC operations. The overlaid pp collisions were generated with the soft QCD processes of Pythia8 using the A3 set of tuned parameters [23] and the NNPDF23LO PDF. Although this set of tuned parameters improves the modelling of minimum-bias data relative to the set used previously (A2 [24]), it overestimates by roughly 3% the hadronic activity as measured using charged-particle tracks. Simulated events were weighted to reproduce the distribution of the average number of interactions per bunch crossing in data, scaled down by a factor 1.03. Many analyses rely on MC samples generated with the ATLAS fast simulation, which uses a parameterized response of the calorimeters [21]. Dedicated corrections to the reconstructed energy and identification efficiencies of electrons and photons were determined for these samples to match the performance observed in the samples using the full simulation of the ATLAS detector. The response of the new reconstruction algorithm was optimized using samples of 40 million single-electron and single-photon events simulated without pile-up. Their transverse energy distribution covers the range from 1GeV to 3 TeV. Smaller samples with a flat hµispectrum between 0 and 60 were also simulated to assess the performance as a function of hµi. Studies presented throughout this paper using MC simulation select electrons originating from Z→ee or J/ψ→ee decays using generator-level information. The matching of reconstructed and generated electron is based on the ID track [25] which can be reconstructed from the primary electron or from secondary particles produced in a material interaction of the primary electron or of final state radiation emitted collinearly. Similarly, reconstructed and generator-level photons are matched based on their distance in η–φspace. 4 Electron and photon reconstruction In replacement of the sliding-window algorithm previously exploited in ATLAS for the reconstructionoffixed-size clustersofcalorimetercells [1,2,26], the offlineelectronandphoton reconstruction –5– 2019 JINST 14 P12006 has been improved to use dynamic, variable-size clusters, called superclusters. While fixed-size clusters naturally provide a linear energy response and good stability as a function of pile-up, dynamic clusters change in size as needed to recover energy from bremsstrahlung photons or from electrons from photon conversions. The calibration techniques described in ref. [3] exploit this advantage of the dynamic clustering algorithm, while achieving similar linearity and stability as for fixed-size clusters. An electron is defined as an object consisting of a cluster built from energy deposits in the calorimeter (supercluster) and a matched track (or tracks). A converted photon is a cluster matched to a conversion vertex (or vertices), and an unconverted photon is a cluster matched to neither an electron track nor a conversion vertex. About 20% of photons at low |η|convert in the ID, and up to about 65% convert at |η| ≈ 2.3. The reconstruction of electrons and photons with |η|<2.5proceeds as shown in figure 1. The algorithm first prepares the tracks and clusters it will use. It selects clusters of energy deposits measured in topologically connected EM and hadronic calorimeter cells [4], denoted topo-clusters, reconstructed as described in section 4.1. These clusters are matched to ID tracks, which are re-fitted accounting for bremsstrahlung. The algorithm also builds conversion vertices and matches them to the selected topo-clusters. The electron and photon supercluster-building steps then run separately using the matched clusters as input. After applying initial position corrections and energy calibrations to the resulting superclusters, the supercluster-building algorithm matches tracks to the electron superclusters and conversion vertices to the photon superclusters. The electron and photon objects to be used for analyses are then built, their energies are calibrated, and discriminating variables used to separate electrons or photons from background are added. The steps are described in more detail below. 4.1 Topo-cluster reconstruction The topo-cluster reconstruction algorithm [4,26] begins by forming proto-clusters in the EM and hadronic calorimeters using a set of noise thresholds in which the cell initiating the cluster is required to have significance ςEM cell ≥4, where ςEM cell =EEM cell σEM noise,cell , EEM cell is the cell energy at the EM scale4and σEM noise,cell is the expected cell noise. The expected cell noise includes the known electronic noise and an estimate of the pile-up noise corresponding to the average instantaneous luminosity expected for Run 2. In this initial stage, cells from the presampler and the first LAr EM calorimeter layer are excluded from initiating proto-clusters, to suppress the formation of noise clusters. The proto-clusters then collect neighbouring cells with significance ςEM cell ≥2. Each neighbour cell passing the threshold of ςEM cell ≥2becomes a seed cell in the next iteration, collecting each of its neighbours in the proto-cluster. If two proto-clusters contain the same cell with ςEM cell ≥2above the noise threshold, these proto-clusters are merged. 4The EM scale is the basic signal scale accounting correctly for the energy deposited in the calorimeter by electromagnetic showers. –6– 2019 JINST 14 P12006 Table 1. Discriminating variables used for electron and photon identification. The usage column indicates if the variables are used for the identification of electrons, photons, or both. For variables calculated in the first EM layer, if the cluster has more than one cell in the φdirection at a given η, the two cells closest in φto the cluster barycentre are merged and the definitions below are given in terms of this merged cell. The sign of d0is conventionally chosen such that the coordinates of the perigee in the transverse plane are (x0,y0)=(−d0sin φ, d0cos φ), where φis the azimuthal angle of the track momentum at the perigee. Category Description Name Usage Hadronic leakage Ratio of ETin the first layer of the hadronic calorimeter to ETof the EM cluster (used over the ranges |η|<0.8and |η|>1.37) Rhad1e/γ Ratio of ETin the hadronic calorimeter to ETof the EM cluster (used over the range 0.8<|η|<1.37) Rhad e/γ EM third layer Ratio of the energy in the third layer to the total energy in the EM calorimeter f3e EM second layer Ratio of the sum of the energies of the cells contained in a 3×7η×φ rectangle (measured in cell units) to the sum of the cell energies in a 7×7 rectangle, both centred around the most energetic cell Rηe/γ Lateral shower width, q(ΣEiη2 i)/(ΣEi)−((ΣEiηi)/(ΣEi))2, where Eiis the energy and ηiis the pseudorapidity of cell iand the sum is calculated within a window of 3×5cells wη2e/γ Ratio of the sum of the energies of the cells contained in a 3×3η×φ rectangle (measured in cell units) to the sum of the cell energies in a 3×7 rectangle, both centred around the most energetic cell Rφe/γ EM first layer Total lateral shower width, q(ΣEi(i−imax)2)/(ΣEi), where iruns over all cells in a window of ∆η≈0.0625 and imax is the index of the highestenergy cell wstot e/γ Lateral shower width, q(ΣEi(i−imax)2)/(ΣEi), where iruns over all cells in a window of 3 cells around the highest-energy cell ws3γ Energy fraction outside core of three central cells, within seven cells fside γ Difference between the energy of the cell associated with the second maximum, and the energy reconstructed in the cell with the smallest value found between the first and second maxima ∆Esγ Ratio of the energy difference between the maximum energy deposit and the energy deposit in a secondary maximum in the cluster to the sum of these energies Eratio e/γ Ratio of the energy measured in the first layer of the electromagnetic calorimeter to the total energy of the EM cluster f1e/γ Track conditions Number of hits in the innermost pixel layer ninnermost e Number of hits in the pixel detector nPixel e Total number of hits in the pixel and SCT detectors nSi e Transverse impact parameter relative to the beam-line d0e Significance of transverse impact parameter defined as the ratio of d0to its uncertainty |d0/σ(d0)|e Momentum lost by the track between the perigee and the last measurement point divided by the momentum at perigee ∆p/p e Likelihood probability based on transition radiation in the TRT eProbabilityHT e Track-cluster matching ∆ηbetween the cluster position in the first layer of the EM calorimeter and the extrapolated track ∆η1e ∆φbetween the cluster position in the second layer of the EM calorimeter and the momentum-rescaled track, extrapolated from the perigee, times the charge q ∆φres e Ratio of the cluster energy to the measured track momentum E/p e – 13 – 2019 JINST 14 P12006 0 5 10 15 20 25 [GeV] T true E 0 0.2 0.4 0.6 0.8 1 Reconstruction efficiency Cluster Track Cluster and track Electron candidate ATLAS Simulation Figure 5. The cluster, track, cluster and track, and electron reconstruction efficiencies as a function of the generated electron ET. the bottom right plot of figure 6. The probability for true unconverted photons to be reconstructed as Si conversions is negligible in comparison. An important reason for using superclusters is the improved energy resolution that superclusters provide by collecting more of the deposited energy. The peaks of the energy response, Ecalib/Etrue, where Etrue is the true energy of the simulated particle prior to any detector simulation, and Ecalib is the calibrated reconstructed energy, do not deviate from one by more than 0.5% for the different particles. To quantify the width (resolution) of the energy response, the effective interquartile range is used, defined as IQE =Q3−Q1 1.349 , where Q1and Q3are the first and third quartiles of the distribution of Ecalib/Etrue, and the normalization factor is chosen such that the IQE of a Gaussian distribution would equal its standard deviation. Comparisons of the resolutions of the calibrated energy response of simulated single electrons, converted photons, and unconverted photons, built using fixed-size clusters and superclusters, are given in figure 7. In particular, figure 7shows the IQE of the two approaches in different regions of |ηtrue|and Etrue T. The reconstructed electrons and photons in these distributions are required to correspond to true primary electrons and photons and to satisfy loose identification requirements. After calibration, the supercluster algorithm shows a significant improvement in resolution compared with the sliding-window algorithm for electrons. In absence of pile-up, an improvement in resolution of up to 20–30% is found in some bins in the endcap region of the detector, as well as in the central region for low-ETelectrons. Similarly, a large improvement in the resolution is seen for converted photons, over 30% in a few bins. For unconverted photons, the overall change in performance is small, due to the generally narrower shower width. However, some improvement is observed for high ETbins in the endcap region. In presence of pile-up, the improvement in resolution still reaches 15 to 20%, depending on ηand ET. – 14 – 2019 JINST 14 P12006 100 200 300 400 500 [GeV] true T E 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 reconstructionγEfficiency for conv. Previous reco. : open Current reco. : full All types 2-track Si 2-track TRT 2-track Si-TRT 1-track Si 1-track TRT ATLAS Simulation 0 10 20 30 40 50 60 〉µ〈 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 reconstructionγEfficiency for conv. All types Previous reco. : open Current reco. : full 2-track Si 2-track TRT 2-track Si-TRT 1-track Si 1-track TRT ATLAS Simulation 0 10 20 30 40 50 60 〉µ〈 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 misreconstructionγEfficiency for unconv. 2-track TRT 1-track TRT Previous reco. : open Current reco. : full ATLAS Simulation Figure 6. The top plot shows the converted photon reconstruction efficiency and contributions of the different conversion types as a function of Etrue T, averaged over hµifor a uniform hµidistribution between 0 and 60. On the bottom, efficiency of the reconstruction of converted photons and contributions of the different conversion types (left), and the probability of an unconverted photon to be mistakenly reconstructed as a converted photon and contributions of the different conversions types (right), both as a function of hµi. An important consideration is the performance of the supercluster reconstruction at different pile-up levels. Figure 8shows the calibrated energy response resolution at different hµilevels for electrons, converted photons, and unconverted photons, in two |η|regions. The topo-cluster noise thresholds for the ‘high-µ’ data sample were tuned for hµi∼40. For electrons and converted photons, the IQE of the supercluster reconstruction generally remains better, although the supercluster-based response is more sensitive to pile-up, as seen by its larger slope as a function of hµi. Part of the reason is that the topo-cluster noise thresholds remain fixed even though hµi changes. For unconverted photons, however, the supercluster reconstruction shows worse IQE for hµi>15. This degradation could be mitigated in particular by limiting the growth of the size of the clusters. 5 Electron and photon energy calibration The energy calibration of electrons and photons closely follows the procedure used in ref. [3], updated for the new energy reconstruction described in section 4. The energy resolution of the – 15 – 2019 JINST 14 P12006 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 true /E calib IQE of E SimulationATLAS |<1.37ηElectron, 0.8<| Supercluster (SC) Fixed-size cluster (SW) 6 78 10 20 30 40 2 10 2 10×23 10 [GeV] true T E 0.6 0.8 1 1.2 SW /IQE SC IQE 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 true /E calib IQE of E SimulationATLAS |<2.2ηElectron, 2.0<| Supercluster (SC) Fixed-size cluster (SW) 6 78 10 20 30 40 2 10 2 10×23 10 [GeV] true T E 0.6 0.8 1 1.2 SW /IQE SC IQE 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 true /E calib IQE of E SimulationATLAS |<1.37η, 0.8<|γConverted Supercluster (SC) Fixed-size cluster (SW) 6 78 10 20 30 40 2 10 2 10×23 10 [GeV] true T E 0.6 0.8 1 1.2 SW /IQE SC IQE 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 true /E calib IQE of E SimulationATLAS |<2.2η, 2.0<|γConverted Supercluster (SC) Fixed-size cluster (SW) 6 78 10 20 30 40 2 10 2 10×23 10 [GeV] true T E 0.6 0.8 1 1.2 SW /IQE SC IQE 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 true /E calib IQE of E SimulationATLAS |<1.37η, 0.8<|γUnconverted Supercluster (SC) Fixed-size cluster (SW) 6 78 10 20 30 40 2 10 2 10×23 10 [GeV] true T E 0.6 0.8 1 1.2 SW /IQE SC IQE 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 true /E calib IQE of E SimulationATLAS |<2.2η, 2.0<|γUnconverted Supercluster (SC) Fixed-size cluster (SW) 6 78 10 20 30 40 2 10 2 10×23 10 [GeV] true T E 0.6 0.8 1 1.2 SW /IQE SC IQE Figure 7. Calibrated energy response resolution, expressed in terms of IQE, for electrons (top), converted photons (middle), and unconverted photons (bottom) simulated with hµi=0. Two representative pseudorapidity ranges are shown. The response resolution for fixed-size clusters based on the sliding window method is shown in dashed red, while the supercluster-based response resolution is shown in full blue. For all plots, the bottom panel shows the ratios between the IQE obtained using the supercluster reconstruction and using the sliding window method. – 16 – 2019 JINST 14 P12006 = 0 , = 0 , = 0 , = 0 ,= 0 , = 0 , = 0 , Figure 8. Calibrated energy response resolution, expressed in terms of IQE, for simulated single electrons (top), converted photons (middle), and unconverted photons (bottom) at different hµilevels. The plots on the left are for the central calorimeter, while the plots on the right are for the endcaps. The response for fixed-size clusters based on the sliding-window algorithm is shown in dashed red, while the supercluster-based response is shown in full blue. The supercluster-based energy response resolution for hµi=0is also given as a black dashed line for comparison. – 17 – 2019 JINST 14 P12006 electron or photon is optimized using a multivariate regression algorithm based on the properties of the shower development in the EM calorimeter. The adjustment of the absolute energy scale using Z→ee decays is updated, together with systematic uncertainties related to pile-up and material effects. The universality of the energy scale is verified using radiative Z-boson decays. 5.1 Energy scale and resolution measurements with Z→ee decays The difference in energy scale between data and simulation is defined as αi, where icorresponds to different regions in η. Similarly, the mismodelling of the energy resolution is parameterised as an η-dependent additional constant term, ci. The corresponding energy scale correction is applied to the data, and the resolution correction is applied to the simulation as follows: Edata,corr =Edata/1+αi,σE EMC,corr =σE EMC ⊕ci, where the symbol ⊕denotes a sum in quadrature. For samples of Z→ee decays, with electrons reconstructed in ηregions iand j, the effect of the energy scale correction on the dielectron invariant mass is given in first order by mdata,corr ij = mdata ij /(1+αij ), with αij =(αi+αj)/2. Similarly, the difference in the simulated mass resolution is given by (σm/m)MC,corr ij =(σm/m)MC ij ⊕cij , with cij =(ci⊕cj)/2. The values of αij and cij are determined by optimizing the agreement between the invariant mass distributions in data and simulation, separately for each (i,j)category. The αiand ciparameters are then extracted from a simultaneous fit of all categories. Two methods areused forthis comparison andthe difference istakenasa systematicuncertainty. In the first method, the best estimates of αij and cij are found by minimizing the χ2of the difference between data and simulation templates. The templates are created by shifting the mass scale in simulation by αij and by applying an extra resolution contribution of cij . In the second method, used as a cross-check, a sum of three Gaussian functions is fitted to the data and simulated invariant mass distributions in each (i,j)region; the αiand ciare extracted from the differences, between data and simulation, of the means and widths of the fitted distributions. Figures 9a and 9b show the results of αiand ciderived in 68 and 24 ηintervals, respectively, separately for 2015, 2016 and 2017. The difference in αifor the different years is mainly due to two effects: variations of the LAr temperature, and the increase of the instantaneous luminosity. The former effect induces a variation in the charge/energy collection, affecting the energy response by about −2%/K[33]. The latter implies an increased amount of deposited energy in the liquid-argon gap that creates a current in the high-voltage lines, reducing the high voltage effectively applied to the gap and introducing a variation of the response of up to 0.1%in the endcap region. A prediction of the different effects that can impact the results is presented in ref. [3]. Given the small size of the observed dependence, well within 0.3%, dedicated energy scale corrections for each data taking year provide an adequate stability of the energy measurement. For the constant term corrections ci, a dependence on the pile-up level is observed through the different values obtained for 2015 to 2017 data; this is addressed in section 5.2. A weighted average of the civalues for the different years is applied in the analyses of the complete dataset. The additional constant term of the energy resolution is typically less than 1%in most of the barrel and between 1%and 2%in the endcap. – 18 – 2019 JINST 14 P12006 2−1.5−1−0.5−0 0.5 1 1.5 2 0.02− 0.01− 0 0.01 0.02 0.03 0.04 i α ATLAS -1 = 13 TeV, 3.2 (2015) + 33.0 (2016) + 44.3 (2017) fbs ee→Z 2017 data 2016 data 2015 data 2−1.5−1−0.5−0 0.5 1 1.5 2 η 0 0.005 0.01 2017 i α - i α (a) 2−1.5−1−0.5−0 0.5 1 1.5 2 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 i c ATLAS -1 = 13 TeV, 3.2 (2015) + 33.0 (2016) + 44.3 (2017) fbs ee→Z 2017 data 2016 data 2015 data Weighted average 2−1.5−1−0.5−0 0.5 1 1.5 2 η 0 0.005 0.01 2017 i - c i c (b) Figure 9. (a) Energy scale factors αiand (b) additional constant term ci, as a function of η. The shaded areas correspond to the statistical uncertainties. The bottom panels show the differences between (a) αiand (b) cimeasured in a given data-taking period and the measurements using 2017 data. Figure 10a shows the invariant mass distribution for Z→ee candidates for data and simulation after the energy scale correction has been applied to the data and the resolution correction to the simulation. No background contamination is taken into account in this comparison, but it is expected to be at the level of 1%over the full shown mass range. The uncertainty band corresponds to the propagation of the uncertainties in the αiand cifactors, as discussed in ref. [3]. Within these uncertainties, the data and simulation are in fair agreement. Figure 10b shows the stability of the reconstructed peak position of the dielectron mass distribution as a function of the average number of interactions per bunch crossing for the data collected in 2015, 2016 and 2017. The variation of the energy scale with hµiis well below the 0.1%level in the data. The small increase of energy with hµiobserved in data is consistent with the MC expectation and is related to the new dynamical clustering used for the energy measurement, as introduced in section 4. 5.2 Systematic uncertainties Several systematic uncertainties impact the measurement of the energy of electrons or photons in a way that depends on their transverse energy and pseudorapidity. These uncertainties were evaluated in ref. [3]. The amount of passive material located between the interaction point and the EM calorimeter is measured using the ratio of the energies deposited by electrons from Z-boson decays in the first and second layer of the EM calorimeter (E1/2). The sensitivity of the calibrated energy to the detector material was re-evaluated to reflect the changes in the reconstruction described above. The systematic uncertainty due to the material description of the innermost pixel detector layer and the services of the pixel detector were updated with regards to ref. [3] using a more accurate description of these systems in the simulation [34]. – 19 – 2019 JINST 14 P12006 500 1000 1500 2000 3 10× Events / 0.5 GeV Calibrated data Corrected MC Scale factor uncert. ATLAS -1 = 13 TeV, 81 fbs ee→Z 80 82 84 86 88 90 92 94 96 98 100 [GeV] ee m 0.95 1 1.05 Data/MC (a) 10 20 30 40 50 60 70 〉µ〈 0.999 0.9995 1 1.0005 1.001 1.0015 1.002 > ee /<m ee m MC data ATLAS -1 = 13 TeV, 81 fbs (b) Figure 10. (a) Comparison between data and simulation of the invariant mass distribution of the two electrons in the selected Z→ee candidates, after the calibration and resolution corrections are applied. The total number of events in the simulation is normalized to the data. The uncertainty band of the bottom plot represents the impact of the uncertainties in the calibration and resolution correction factors. (b) Relative variation of the peak position of the reconstructed dielectron mass distribution in Z→ee events as a function of the average number of interactions per bunch crossing. The error bars represent the statistical uncertainties. The dependence of the constant term on the amount of pile-up, observed in figure 9b, is explained by the larger pile-up noise predicted by the simulation, compared with that observed in the data. Figure 11 shows an example of the evolution of the second central moment of the cell energy deposit in data and simulation as a function of µfor the second layer and 1.0<|η|<1.1 assuming φsymmetry. The contribution of the pile-up noise varies linearly with √µ, while the electronic noise remains constant. An average difference of 10%between the pile-up noise in data and simulation is observed. This mismodelling is absorbed in the ciparameters for electrons of ET∼40GeV, the average ETvalue for electrons from Z→ee decays used to derive the energy corrections. The two methods used for the extraction of the energy resolution corrections, described in section 5.1, are compared andthe full difference is taken as an uncertainty in the energy resolution. This uncertainty amounts to up to 0.2% in the barrel and is due to the different sensitivities of the two methods to the pile-up. The impact of a 10%difference in pile-up noise at a different energy is propagated to the energy resolution uncertainty relying on the predicted dependence of the pile-up noise effect as a function of the energy. For electrons and photons in the transverse energy range 30–60GeV, the uncertainty in the energy resolution is of the order of 5% to 10%. In order to mimic the pile-up noise estimation in the simulation, the pile-up rescaling factor, described in section 3, is changed from 1.03 to 1.2 for the 48b filling scheme and to 1.3 for the 8b4e filling scheme. A systematic uncertainty in the energy scale is derived comparing the results obtained with the two pile-up reweighting factors; it is of the order of 2×10−4in the barrel and of 5×10−4in the endcap. The total systematic uncertainty in the energy scale amounts to 4×10−4in the barrel and 2×10−3 in the endcap. 5.3 Validation of the photon energy scale with Z→``γ decays The energy scale corrections extracted from Z→ee decays, as described in section 5.1, are applied to correct the photon energy scale. A data-driven validation of the photon energy scale corrections – 20 – 2019 JINST 14 P12006 15 20 25 30 35 40 45 50 〉µ〈 0 1000 2000 3000 4000 5000 6000 ] 2 [MeV 2 RMS Simulation Fit to simulation Data Fit to data ATLAS = 13 TeVs2016 data, | < 1.1ηEMB Layer 2, 1.0 < | Figure 11. Evolution of the squared noise as a function of hµiin data (red points) and simulation (blue triangles), for one particular ηbin in the second layer of the EM calorimeter. The lines show the result of linear fits to the points for hµi∈ [15,45]and the dotted lines show the extrapolation to higher hµi. is performed using radiative decays of the Zboson, probing mainly the low-energy region. Residual energy scale factors for photons, ∆α, are derived by comparing the mass distribution of the ``γ system in data and simulation after applying the Z-based energy scale corrections. The mass distribution of the ``γ system in the simulation is modified by applying ∆αto the photon energy and the value of ∆αthat minimizes the χ2comparison between the data and the simulation is extracted. If the energy calibration is correct, ∆αshould be consistent with zero within the uncertainties described in section 5.2. An alternative method based on a binned extended maximum-likelihood fit with an analytic function to describe the mass distribution is used, and gives consistent results. The electron and muon channels are analysed separately. In the electron channel, the electron energy scale uncertainty is accounted for in the determination of the residual photon energy scale. The electron and muon results are found to agree, and are combined. Figure 12 shows the measured ∆αas a function of ETand |η|, separately for converted and unconverted photons. The dominant sources of uncertainty in the extrapolation to photons of the energy corrections derived in Z→ee decays are related to the amount of passive material in front of the EM calorimeter, and to the intercalibration of the calorimeter layers. The value of ∆αis consistent with zero within about two standard deviations at most. 5.4 Energy scale and resolution corrections in low-pile-up data Special data with low pile-up were collected in 2017 at 13 TeV, as described in section 3. Energy scale factors are derived for this sample using the baseline method, described in section 5.1. The measurement is done in 24 ηregions given the small size of the sample. An alternative approach, used for validation, consists of measuring the energy scale factors using high-pile-up data and extrapolating the results to the low-pile-up conditions. Two main effects are considered in the extrapolation, namely the explicit dependence of the energy corrections on – 21 – 2019 JINST 14 P12006 15-20 20-30 > 30 [GeV] T γ E 10− 8− 6− 4− 2− 0 2 4 6 8 10 3 10× α∆ ATLAS γUnconverted γµµ → + Zγ ee→Z -1 = 13 TeV, 81 fbs Calibration uncertainty measurementγ ll→Z 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 |η| 15− 10− 5− 0 5 10 15 3 10× α∆ ATLAS γUnconverted γµµ → + Zγ ee→Z -1 = 13 TeV, 81 fbs Calibration uncertainty measurementγ ll→Z 15-20 20-30 > 30 [GeV] T γ E 10− 8− 6− 4− 2− 0 2 4 6 8 10 3 10× α∆ ATLAS -1 = 13 TeV, 81 fbs γConverted γµµ → + Zγ ee→Z Calibration uncertainty measurementγ ll→Z 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 |η| 15− 10− 5− 0 5 10 15 3 10× α∆ ATLAS γConverted γµµ → + Zγ ee→Z -1 = 13 TeV, 81 fbs Calibration uncertainty measurementγ ll→Z Figure 12. Residual photon energy scale factors, ∆α, for unconverted (top) and converted (bottom) photons as a function of the photon transverse energy ET(left) and pseudorapidity |η|(right), respectively. The points show the measurement with its total uncertainty and the band represents the full energy calibration uncertainty for photons from Z→``γ decays. hµi, and differences between the clustering thresholds used for the two samples; other effects are sub-leading and are treated as systematic uncertainties. To evaluate the first effect, the high-pile-up energy scale corrections are measured in five intervals of hµiin the range 20 <hµi<60, in each of the 24 ηregions considered for the low-pileup sample. The results are parameterized using a linear function, which is extrapolated to hµi=2. Over this range, the energy correction is found to vary by about 0.01% in the barrel, and by about 0.1% in the endcap. The statistical uncertainty in the extrapolation is about 0.05% in each ηregion. The procedure is illustrated in figure 13, for representative ηregions in the barrel and in the endcap. Secondly, as described in section 4, the low-pile-up data were reconstructed with topo-cluster noise thresholds corresponding to µ=0, while the standard runs used thresholds corresponding to µ=40. This results in an increased cluster size and enhanced energy response for the low-pile-up samples. The difference between the enhancements in data and simulation is measured using Zboson decays, and a correction applied. The correction amounts to about 2×10−3in the barrel and 4×10−3in the endcap, with a typical uncertainty of 3×10−4. Figure 14a shows the comparison between the energy scale factors derived from low-pile-up data and extrapolated from high-pile-up data after correcting for the noise threshold effect. The observed difference is of the order of 0.1% in the barrel region and increases to 0.5% in the endcap region. – 22 – 2019 JINST 14 P12006 25 GeV, the Z→``γ MC sample with the selection described in section 3.1 is used as a signal. The corresponding background sample is obtained from data consisting of Z+jets events collected using a similar event selection, but with relaxed requirements on the dilepton and dilepton+photon invariant masses m`` and m``γ. Above ET=25 GeV, the inclusive-photon production MC sample described in section 3.2 is compared with a dijet background MC sample that is enriched in highETenergy deposits using a generator-level filter. No isolation selection is applied to the training samples, and the shower shape variables are corrected to match the shower shapes observed in data using the correction procedure described in ref. [1]. Figures 18 and 19 show the result of the Tight identification optimization in terms of the efficiencies as a function of ETfor the signal and background MC training samples. The optimized selection, labelled ET-dependent, is compared with a reference selection that uses criteria that do not change with ET(ET-independent). The new, ET-dependent Tight identification allows the efficiencies of lowand high-ETphoton regions to be tuned separately. The Tight identification is tuned to give a ∼20% higher efficiency at low ET, and an improved background rejection at high ET. The hµidependence of the photon identification is depicted in figure 20 for photons from Z→``γ decays. 0.5 0.6 0.7 0.8 0.9 1 1.1 Tight efficiency -dependent ID T E -independent ID T E -dependent ID T E -independent ID T E γll→Z Inclusive photons Simulation ATLAS = 13 TeVs Unconverted γ |η| < 2.37, excluding crack Loose isolation preselection 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 1.2 Efficiency ratio 0.5 0.6 0.7 0.8 0.9 1 1.1 Tight efficiency -dependent ID T E -independent ID T E -dependent ID T E -independent ID T E γll→Z Inclusive photons Simulation ATLAS = 13 TeVs Converted γ |η| < 2.37, excluding crack Loose isolation preselection 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 1.2 Efficiency ratio Figure 18. Efficiencies of the Tight photon identification for unconverted (left) and converted (right) signal photons, plotted as a function of photon ET. The signal events are taken from the sample of Z→``γ photons with ET<25 GeV, and from inclusive-photon production above 25 GeV. In each case, the ETindependent and ET-dependent selections are compared. The Loose isolation (see section 8.2) is applied as a preselection. For both plots, the bottom panel shows the ratios between the ET-dependent and the ET-independent identification efficiencies. 7.2 Efficiency of the photon identification To assess the performance of the (ET-dependent) Tight photon identification on data, three photon efficiency measurements are performed using distinct data samples. The first uses an inclusivephoton production data selection, the second uses photons radiated from leptons in Z→``γ decays, and the third uses electrons from Z→ee decays, with a method that transforms the electron shower – 29 – 2019 JINST 14 P12006 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Tight efficiency -dependent ID T E -independent ID T E -dependent ID T E -independent ID T E ll+jets→Z dijet production Simulation ATLAS = 13 TeVs Unconverted γ |η| < 2.37, excluding crack Loose isolation preselection 20 40 100 200 400 1000 [GeV] T E 0.8 1 1.2 Efficiency ratio 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Tight efficiency -dependent ID T E -independent ID T E -dependent ID T E -independent ID T E ll+jets→Z dijet production Simulation ATLAS = 13 TeVs Converted γ |η| < 2.37, excluding crack Loose isolation preselection 20 40 100 200 400 1000 [GeV] T E 0.8 1 1.2 Efficiency ratio Figure 19. Efficiencies of the Tight photon identification for unconverted (left) and converted (right) background photons from jets, plotted as a function of photon ET. The background is taken from Z→``+jets production below 25 GeV, and filtered dijet production above 25GeV. In each case, the ET-independent and ET-dependent selections are compared. The Loose isolation (see section 8.2) is applied as a preselection. For both plots, the bottom panel shows the ratios between the ET-dependent and the ET-independent identification efficiencies. 10 20 30 40 50 60 70 µ 0.75 0.8 0.85 0.9 0.95 1 Data efficiency ATLAS -1 =13 TeV, 44.3 fbs γ ll→Z < 40 GeV T 20 GeV < E | < 2.37η| < 1.37 or 1.52 < |η|γUnconverted Loose isolation preselection 10 20 30 40 50 60 70 〉µ〈 0.9 0.95 1 1.05 1.1 Data / MC 10 20 30 40 50 60 70 µ 0.75 0.8 0.85 0.9 0.95 1 Data efficiency ATLAS -1 =13 TeV, 44.3 fbs γ ll→Z < 40 GeV T 20 GeV < E | < 2.37η| < 1.37 or 1.52 < |η|γConverted Loose isolation preselection 10 20 30 40 50 60 70 〉µ〈 0.9 0.95 1 1.05 1.1 Data / MC Figure 20. Photon identification efficiency as a function of hµifor unconverted (left) and converted photons (right), as measured by the radiative Zmethod, for photons with 20 <ET<40 GeV. Backgrounds, which are not subtracted in this plot, are estimated to be below 1%. The error bars show the statistical uncertainties. For both plots, the bottom panel shows the data-to-simulation ratios. shapes to resemble the photon shower shapes. These efficiency measurements are described in detail in ref. [1], and summarized below. All three procedures measure photons that are isolated, using the Loose working-point definition (see section 8.2). The three measurements use a common method to characterize the imperfect modelling of shower shapes in simulated samples, in order to estimate its impact on the efficiency measurement – 30 – 2019 JINST 14 P12006 in data. Nominally, the MC shower shapes are compared with data in control regions enriched in real photons and corrected by applying a simple shift to the distributions, whose magnitude is determined by a χ2minimization procedure. However, some data-MC differences cannot be corrected by this procedure, such as the widths of the distributions. In order to estimate any residual data-MC differences, the χ2minimisation is repeated considering only the tail of the distribution, defined as the region containing 30% of the distribution on the side closer to the identification cut value. The shift value obtained when comparing the data and simulation tails is used to define a systematic uncertainty in the modelling of the shower shapes, and is derived for all variables for which a mismodelling is observed. Four variations are defined using sets of correlated variables; the variables within each set are shifted together: {Rhad}, {Rφ}, {Rη,wη2}, and {ws3,fside,wstot}. The result is equivalent to four sets of MC simulated samples, which can be used to assign systematic uncertainties for mismodelling effects that impact the data measurement, and which are considered to be uncorrelated variations. The method using Z→``γ decays selects data as described in section 3.1. Additional requirements on the invariant mass of the three-body system, 80 <m``γ <100 GeV, and on the lepton-pair invariant mass, 40 <m`` <83 GeV, select radiative Z-boson decays while rejecting backgrounds from Z+γand Z+jets production. The efficiency and purity of the samples with and without the Tight identification requirement are determined from fits of signal and background templates, extracted from simulated Z→``γ and Z+jets events, to the observed three-body invariant-mass distribution. The systematic uncertainties in the photon efficiency measurement using Z→``γ decays include a closure test using simulated signal and background samples to assess the validity of the measurement. To assess the impact of simulation mismodelling, the measurement is repeated comparing the Powheg-Pythia8 and Sherpa Z→`` samples and the difference is taken as a systematic uncertainty. The shower shape correction uncertainties are considered by repeating the measurement with each of the four sets of modified simulation samples, and the observed differences are added in quadrature. Finally, as a test of the background description, the fit range of the m``γ distribution is varied from its nominal value of [65,105]GeV using two variations, [45,95]GeV and [80,120]GeV, and the efficiency differences are assigned as a systematic uncertainty. The method to extract the photon efficiency using inclusive-photon production relies on data collected with prescaled photon triggers that feature a Loose identification requirement, as described in section 3.1. This data sample contains a mixture of real photons and backgrounds from jet production, and a matrix method is used to extract the photon efficiency. The matrix method constructs four regions by categorizing Loose photon candidates according to whether they pass or fail the Tight identification, and whether they pass or fail track-based isolation cuts. The four regions contain eight unknowns (i.e. the numbers of signal and background events in each region); if the isolation efficiencies for signal and background from each region are known, the efficiency for Loose photons to pass the Tight identification can be extracted. The isolation efficiencies for loosely and tightly identified signal photons are determined from the Monte Carlo samples, and the isolation efficiencies for backgrounds are obtained in a jet-enriched control region constructed by inverting identification criteria. Finally, the efficiency for reconstructed photon candidates to pass the Loose identification is determined from simulation, as this contribution is not measured in data by this method. The magnitude of the correction is typically less than 5%, and smaller at high ET. – 31 – 2019 JINST 14 P12006 Systematic uncertainties assigned to the matrix method include a closure uncertainty that quantifies the agreement between the background isolation efficiencies derived in the data control region and in the regions to which they are applied. This effect is estimated using simulation, and is the largest source of uncertainty in the measurement. The robustness of the method is tested by varying the track-based isolation requirement, and assigning any difference in measured efficiency as a systematic uncertainty. The impact of uncertainties in the shower shape corrections is estimated using simulation; the effects of the four shower shape variations described above are added in quadrature. Finally, an uncertainty is assigned for a potential mismodelling in the MCbased correction to extrapolate from Loose to reconstructed photons. This uncertainty is based on the Loose identification efficiency measured with radiative photons in Z→``γ events. Photon efficiencies can be estimated in a data sample of electrons from Z→ee decays whose shower shape variables have been modified to resemble photon shower shapes, a technique referred to as the electron extrapolation method. This efficiency measurement, described in ref. [1], uses the Z→ee sample defined in section 3.1, with the photon Loose isolation requirement applied to the electron candidates. Electron shower shape variables are modified using a Smirnov transform [38] derived from simulated Z→ee and inclusive-photon production samples. The candidate electrons in data contain a small background from W+jets and multijet production; this background is subtracted by fitting simulated signal samples and background templates derived from data control regions to the mee data distributions. The electron candidates are counted for events in the range 70 <mee <110 GeV, and the efficiencies are measured using the tag-and-probe method as described in section 6. The systematic uncertainties in the electron extrapolation method are as follows. First, a closure test is performed to determine whether the transformed electrons can reproduce the expected photon efficiency, using the simulation and in the absence of background. The difference in relative efficiency, which can be as high as 3%, is applied as a correction to the measured data efficiency, and the magnitude of the correction is assigned as the systematic uncertainty. Systematic effects that affect the Smirnov transformations include the fraction of fragmentation photons in the simulated inclusive-photon sample, which is varied by ±50%, and the predicted fraction of true converted photons, which is varied by ±10%, to assess the impact of the imperfect simulation on the efficiency measurement. The uncertainty in the modelling of identification variables in simulation is assessed by defining Smirnov transformations for each of the four sets of variations of the shower shape modelling, recalculating the efficiency for each case; the total modelling uncertainty is taken as the sum in quadrature of the individual variations. The uncertainty due to the limited size of the MC samples used to derive the Smirnov transformations is assessed using the bootstrap method. Finally, the uncertainty associated with the subtraction of the W+jets and multijet backgrounds in the signal region is tested by reducing the level of background through a restriction of the selected invariant-mass range to 80 <m`` <100 GeV, and repeating the measurement procedure. The resulting difference in the measured efficiency is taken as the systematic uncertainty. The three efficiency measurements are compared with MC simulation in order to obtain scale factors, in bins of ETand |η|, that are used to correct the MC simulations so that the simulations closely resemble data. Before determining these scale factors, the shower shapes in these MC simulations were corrected to match data using the procedure described in ref. [1]. – 32 – 2019 JINST 14 P12006 Figures 21 and 22 depict the Tight identification efficiencies for unconverted and converted photons as measured with the three efficiency methods. The data/MC scale factors are also shown for each measurement separately. The three efficiency measurements are performed using different processes, with different event topologies that may impact the photon efficiency. Despite this fact, the efficiency measurements are compatible within their statistical and systematic uncertainties. 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 0.6 <|η| <, 0.0γUnconverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 1.37 <|η| <, 0.6γUnconverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 1.81 <|η| <, 1.52γUnconverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 2.37 <|η| < , 1.81γUnconverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination Figure 21. The photon identification efficiency, and the ratio of data to MC efficiencies, for unconverted photons with a Loose isolation requirement applied as preselection, as a function of ETin four different |η| regions. The combined scale factor, obtained using a weighted average of scale factors from the individual measurements, is also presented; the band represents the total uncertainty. The scale factors from each of the three efficiency measurements are combined using a weighted average. The statistical and systematic uncertainties are assumed to be uncorrelated between the methods. The total uncertainty of the combined scale factors ranges between 7% at low ETand 0.5% at high ETfor unconverted photons, and between 12% (low ET) and less than 1% (high ET) for converted photons. For ET> 1.5TeV, where no measurement is performed, the scale factor measured in the ETbin [0.25,1.5] TeV is used, with the same uncertainty. – 33 – 2019 JINST 14 P12006 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 0.6 <|η| <, 0.0γConverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 1.37 <|η| <, 0.6γConverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 1.81 <|η| <, 1.52γConverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination 0.5 0.6 0.7 0.8 0.9 1 Data efficiency ATLAS 1− = 13 TeV, 81 fbs 2.37 <|η| < , 1.81γConverted Matrix method Radiative Z Electron extrapolation 10 20 40 100 200 400 1000 [GeV] T E 0.9 1 1.1 Data / MC Combination Figure 22. The photon identification efficiency, and the ratio of data to MC efficiencies, for converted photons with a Loose isolation requirement applied as preselection, as a function of ETin four different |η| regions. The combined scale factor, obtained using a weighted average of scale factors from the individual measurements, is also presented; the band represents the total uncertainty. 8 Electron and photon isolation The activity near leptons and photons can be quantified from the tracks of nearby charged particles, or from energy deposits in the calorimeters, leading to two classes of isolation variables. The raw calorimeter isolation [2] (Eisol T,raw) is built by summing the transverse energy of positiveenergy topological clusters whose barycentre falls within a cone centred around the electron or photon cluster barycentre. The topological cluster energy scale is the EM scale. The raw calorimeter isolation includes the EM particle energy (ET,core), which is subtracted by removing the energy of the EM calorimeter cells contained in a ∆η×∆φ=5×7(in EM-middle-layer units) rectangular cluster around the barycentre of the EM particle cluster. The advantage of this simple method is – 34 – 2019 JINST 14 P12006 a stable subtraction for real or fake/non-prompt objects for any transverse momentum and pileup. The disadvantage is that it does not subtract all the EM particle energy and an additional leakage correction is needed. This leakage is parameterized as a function of ETand |η|using MC samples of single electrons or photons without pile-up. Additionally, a correction for the pile-up and underlying-event contribution to the isolation cone is also estimated [39]. Finally, the fully corrected calorimeter isolation variable is computed as: EconeXX T=EisolXX T,raw −ET,core −ET,leakage(ET, η, ∆R)− ET,pile-up(η, ∆R), where XX refers to the size of the employed cone, ∆R=XX/100. A cone size ∆R=0.2is used for the electron working points whereas cone sizes ∆R=0.2and 0.4 are used for photon working points. The track isolation variable (pconeXX T) is computed by summing the transverse momentum of selected tracks within a cone centred around the electron track or the photon cluster direction. Tracks matched to the electron or converted photon are excluded. Since for electrons produced in the decay of high-momentum heavy particles, other decay products can be very close to the electron direction, the track isolation for electrons is defined with a variable cone size (pvarconeXX T) — the cone size shrinks for larger transverse momentum of the electron: ∆R=min 10 pT[GeV],∆Rmax, where ∆Rmax is the maximum cone size (typically 0.2). The tracks considered are required to have pT>1GeV and |η|<2.5, at least seven silicon (Pixel + SCT) hits, at most one shared hit (defined as nsh Pixel +nsh SCT/2, where nsh Pixel and nsh SCT are the numbers of hits assigned to several tracks in the Pixel and SCT detectors), at most two silicon holes (i.e. missing hits in the pixel and SCT detectors) and at most one pixel hole. In addition, for electron isolation, the tracks are required to have a loose vertex association, i.e. the track was used in the primary vertex fit, or it was not used in any vertex fit but satisfies |∆z0|sin θ < 3mm, where |∆z0|is the longitudinal impact parameter relative to the chosen primary vertex; for photon isolation, all selected tracks satisfying |∆z0|sin θ < 3mm are used. In this section, the isolation efficiency measurements are illustrated with the data recorded in 2017; nevertheless, the measurements are performed for the full high-µdataset described in section 3.1. 8.1 Electron isolation criteria and efficiency measurements The implementation of isolation criteria is specific to the physics analysis needs, as it results from a compromise between a highly-efficient identification of prompt electrons, isolated or produced in a busy environment, and a good rejection of electrons from heavy-flavour decays or light hadrons misidentified as electrons. The different electron-isolation working points used in ATLAS are presented in table 2. The working points can be defined in two different ways, targeting a fixed value of efficiency or with fixed cuts on the isolation variables. The Gradient working point is designed to give an efficiency of 90% at pT=25 GeV and 99% at pT=60 GeV, uniform in η. The requirements on – 35 – 2019 JINST 14 P12006 Econe20 Tand pvarcone20 T(cut maps) for this working point are derived from J/ψ→ee (ET<15 GeV) and Z→ee (ET>15 GeV) MC simulations and Tight identification requirements. The three other working points, HighPtCaloOnly, Loose and Tight, have a fixed requirement on the calorimeter and/or the track isolation variables. Figure 23 shows the electron isolation efficiency measured in data recorded in 2017 and the corresponding data-to-MC simulation ratios as a function of the electron ETand η, and of the number of interactions per bunch crossing for the isolation working points summarized in table 2. The pile-up correction to the calorimeter isolation is applied, and reduces the dependence of the isolation efficiency by about a factor of five. These results are obtained using a sample enriched in Z→ee events, where the electrons satisfy the Medium identification. The method used to compute the electron isolation efficiency and the associated uncertainties are described in ref. [2]. For Gradient, a jump in the efficiency is observed at the transition point of 15GeV because the value of the isolation efficiency is process dependent: the cut maps are optimized with J/ψ→ee events below 15 GeV, while the measurement is performed with Z→ee events in the full range. The Tight operating point gives the highest background rejection below 60 GeV and the most significant difference in shape in η. As the name suggests, HighPtCaloOnly gives the highest rejection in the high-ETregion (ET>100GeV). The Gradient and Tight operating points give the highest pile-up dependency, the isolation efficiency decreasing from ∼95% at low hµito ∼85% when hµi is around 70–80. The overall differences between data and MC simulation are less than approximately 1–5% depending on the working point, with the largest difference observed for Tight isolation. For electrons with EThigher than 500GeV no measurement can be performed because of the limited number of data events, and the results from the ETbin [300,500] GeV are used with an additional systematic uncertainty varying betwen 0.1% and 1.7%, depending on the isolation working point. The overall scale factor uncertainties range from about 5% for electrons with ETbelow 7 GeV, to less than 0.5% towards high ET. 8.2 Photon isolation criteria and efficiency measurements Three photon isolation operating points are defined using requirements on the calorimeter and track isolation variables, as summarized in table 3. For the calorimeter-based photon isolation variables a discrepancy between the peak positions of their distributions in data and simulation has been observed since Run 1 [40], pointing to a mismodelling in simulation of the lateral profile development of the electromagnetic showers. As a result, the photon isolation efficiencies in data and simulations disagree, leading to scale factors significantly different from 1. These discrepancies are mitigated by applying data-driven shifts to the calorimeter isolation variables for photons in simulation. The shifts are obtained by performing fits to the calorimeter isolation variable distribution, using Crystal Ball pdfs [41], in regions dominated by real photons, in data and simulation. The fits are performed in bins of photon η,ETand conversion status, separately for Econe20 Tand Econe40 Tisolation variables. The difference in the fitted peak values between data and simulation defines the shift value, which is added to the photon calorimeter isolation values in simulation. Figure 24 illustrates the data-driven shifts obtained with 2017 data and the Pythia8 simulation for the Econe20 Tand Econe40 Tisolation variables in two ηregions. Figure 25 shows the – 36 – 2019 JINST 14 P12006 Table 2. Definition of the electron isolation working points and isolation efficiency . In the Gradient working point definition, the unit of pTis GeV. All working points use a cone size of ∆R=0.2for calorimeter isolation and ∆Rmax =0.2for track isolation. Working point Calorimeter isolation Track isolation Gradient =0.1143 ×pT+92.14% (with Econe20 T)=0.1143 ×pT+92.14% (with pvarcone20 T) HighPtCaloOnly Econe20 T<max(0.015 ×pT,3.5GeV)— Loose Econe20 T/pT<0.20 pvarcone20 T/pT<0.15 Tight Econe20 T/pT<0.06 pvarcone20 T/pT<0.06 0.5 0.6 0.7 0.8 0.9 1 Data efficiency Gradient HighPtCaloOnly Loose Tight ATLAS -1 = 13 TeV, 44.3 fbs Electrons, Medium ID [GeV] T E 0.95 1 1.05 Data / MC 5 10 20 30 40 50 100 200 300 400 0.75 0.8 0.85 0.9 0.95 1 Data efficiency Gradient HighPtCaloOnly Loose Tight ATLAS -1 = 13 TeV, 44.3 fbs > 4.5 GeV T Electrons, Medium ID, E 2−1.5−1−0.5−0 0.5 1 1.5 2 η 0.98 1 1.02 Data / MC 0.75 0.8 0.85 0.9 0.95 1 Data efficiency Gradient HighPtCaloOnly Loose Tight ATLAS -1 = 13 TeV, 44.3 fbs > 4.5 GeV T Electrons, Medium ID, E 10 20 30 40 50 60 70 80 〉µ〈 0.95 1 1.05 Data / MC Figure 23. Efficiency of the different isolation working points for electrons from inclusive Z→ee events as a function of the electron ET(top left), electron η(top right) and the number of interactions per bunch crossing hµi(bottom). The electrons are required to fulfil the Medium selection from the likelihood-based electron identification. The lower panel shows the ratio of the efficiencies measured in data and in MC simulations. The total uncertainties are shown, including the statistical and systematic components. – 37 – 2019 JINST 14 P12006 Table 3. Definition of the photon isolation working points. Working point Calorimeter isolation Track isolation Loose Econe20 T<0.065 ×ETpcone20 T/ET<0.05 Tight Econe40 T<0.022 ×ET+2.45 GeV pcone20 T/ET<0.05 TightCaloOnly Econe40 T<0.022 ×ET+2.45 GeV — 30 40 50 100 200 300 [GeV] T E 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1 1.2 1.4 [GeV] MC - peak Data peak ATLAS -1 = 13 TeV, 44.3 fbs Inclusive photons cone20 T EγConverted |<0.60η| |<2.37η1.81<| 30 40 50 100 200 300 [GeV] T E 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1 1.2 1.4 [GeV] MC - peak Data peak ATLAS -1 = 13 TeV, 44.3 fbs Inclusive photons cone20 T EγUnconverted |<0.60η| |<2.37η1.81<| 30 40 50 100 200 300 [GeV] T E 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1 1.2 1.4 [GeV] MC - peak Data peak ATLAS -1 = 13 TeV, 44.3 fbs Inclusive photons cone40 T EγConverted |<0.60η| |<2.37η1.81<| 30 40 50 100 200 300 [GeV] T E 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1 1.2 1.4 [GeV] MC - peak Data peak ATLAS -1 = 13 TeV, 44.3 fbs Inclusive photons cone40 T EγUnconverted |<0.60η| |<2.37η1.81<| Figure 24. The data-driven shifts for Econe20 T(top) and Econe40 T(bottom) obtained with 2017 data and Pythia8 MC simulations; the Tight isolation working point is applied as preselection to decrease the level of background. The results are shown as a function of photon ET, in two ηregions of the detector (|η|<0.6and 1.81 <|η|<2.37), separately for converted (left) and unconverted (right) photons. Only the uncertainties associated with the fit parameters are shown. distribution of the Econe40 Tisolation variable in 2017 data and simulation, using Z→``γ events after the data-driven shifts are applied. The photon isolation efficiency is studied in two main signatures: radiative Zdecays (valid for 10 <ET<100 GeV) and inclusive photons (used in the 25 GeV <ET<∼1.5 TeV range). 8.2.1 Measurement of photon isolation efficiency with radiative Zdecays As detailed in section 7, final-state radiation in Z-boson decays provides a clean environment to probe photons in the low-ETrange. Using the same method as for the photon identification, photon – 38 – 2019 JINST 14 P12006 charge is heavily used as a selection criterion in measurements with the ATLAS experiment, and hence understanding the effects of charge misidentification is important. Some specific signatures also require the suppression of electron charge misidentification in order to reduce background. 9.1 Suppression of electron charge misidentification The suppression of electron charge misidentification is based on the output discriminant of a boosted decision tree (BDT). A previous version, optimized for data recorded in 2015 and 2016, rejected 90% of electrons with incorrectly reconstructed charge, removing only 3% of electrons with correctly reconstructed charge [2]. The optimization was based on simulated electrons and showed a higher rejection than observed in data. In the following, a re-optimization of the BDT is described. Data from Z→ee decays are used to reduce efficiency losses due to mismodelling of the input variables in the BDT training. Furthermore, additional input variables have been studied. To select a relatively clean sample of electrons with correctly and incorrectly reconstructed charge, one of the electrons is restricted to |η|<0.6, required to satisfy Tight identification and to pass the 97% operating point of the previous BDT discriminant. These requirements minimize charge misidentification for this electron. Any additional reconstructed electron in the event is used to train the BDT, as a signal electron if it has an electric charge different from the first electron, and as a background electron if the electric charge is the same. To reduce background from converted photons from initialor final-state radiation, the invariant mass of any pairs of electrons must lie within 5GeV of 90 GeV in opposite-charge events and within 5 GeV of 88 GeV in same-charge events. The lower value used in same-charge events accounts for the fact that electrons with the incorrect charge have a higher probability for energy loss as discussed in section 9.2 and illustrated in figure 30a. Input quantities to the BDT are the electron ETand η, and a set of additional variables. In decreasing order of separation power, these are: the transverse impact parameter multiplied by the electron electric charge q×d0, the average charge of all tracks matched to the electron weighted by their number of hits in the SCT detector ¯qSCT,E/pand ∆φres. With ¯qSCT the BDT includes for the first time the reconstructed properties of additional tracks in the vicinity of the electron, which improves rejection in cases where the incorrect track is chosen as the primary electron track. The efficiency of the requirement on the BDT is 98% in Z→ee events for electrons satisfying Mediumor Tight identificationwith theTightisolationrequirement, andthat havethecorrect electric charge. Approximately 90% of electrons with the same identification and isolation requirements but incorrect electric charge are removed. This re-optimization of the BDT variables has improved the efficiency of the selection criterion, leaving the rejection of electrons with misidentified charge unchanged. 9.2 Measurement of the probability for charge misidentification The probability for electron charge misidentification is measured in seven bins in ηand six ET bins in the range 20 GeV <ET<95 GeV in Z→ee events. The events were collected with the dielectron triggers discussed in section 3.1 with transverse momentum thresholds of 17 GeV or less and Loose trigger identification, allowing the measurement to be extended to lower values of ETand looser identification criteria than previous measurements. Both electrons in the event are selected – 45 – 2019 JINST 14 P12006 60 70 80 90 100 110 120 [GeV] ee m 0 10000 20000 30000 40000 50000 Events / GeV 0 20 40 60 80 100 120 140 160 Data opposite-charge Background opposite-charge Data same-charge Background same-charge ATLAS -1 = 13 TeV, 81 fbs <60 GeV, T 20 GeV<E |<1.37, 1 η0.75<| |<1.70 2 η1.52<| (a) 0.2−0.15−0.1−0.05−0 0.05 0.1 0.15 0.2 true T p true T -p reco T p 4− 10 3− 10 2− 10 1− 10 1 Charge misid. probability |<1.37η 0.00<| |<2.30η 1.52<| |<2.47η 2.30<| ATLAS Simulation = 13 TeVs (b) Figure 30. (a) Dielectron invariant mass distribution of events from Z→ee production used for the measurement of electron charge misidentification efficiencies. The events are selected with a same-charge or an opposite-charge requirement where one electron falls into 0.75 <|η|<1.37 and the other into 1.52 <|η|<1.70. Both electrons have 20 GeV <ET<60GeV. The estimated background from misidentified electrons and contributions from final state radiation are shown as a continuous line with its uncertainty as a shaded band. (b) Charge misidentification probabilities as a function of the energy measurement residual, for electrons meeting the Tight identification and Tight isolation criteria, in simulated Z→ee events. Only statistical uncertainties are shown. with the same identification and isolation criteria and, respectively, fall into bins iand jin η, ET, yielding Nij Z→ee events. Their invariant mass must lie within 10GeV of the nominal Z-boson mass. The probabilities of the electron charge misidentification in bins iand j,iand j, maximize the Poisson probability Pλij |nsc ij , where: λij =i1−j+1−ijNij +Bsc ij, and nsc ij is the number of same-charge Z→ee events. The number of background events in the sample where both electrons have the same electric charge, Bsc ij , consists of misidentified electrons from multijet production and electrons from converted photons from the aforementioned finalstate radiation. The two components are estimated in a sideband subtraction and from simulation, respectively. The selected data and the estimated background is shown in figure 30a for an example bin. Sources of systematic uncertainties in the measurement are the estimation of the background from multijet production and final-state radiation, and the restriction of the dielectron invariant mass. Possible biases in the experimental method used to perform the measurement are evaluated by comparing the charge misidentification probability obtained in the likelihood maximization in simulation with those obtained using generator-level information. The kinematic range of ET>95 GeV is particularly relevant for searches for physics beyond the Standard Model with same-charge signatures. For a measurement with high granularity, the double differential charge misidentification probabilities are factorized into an ηand an ET-dependent part. This approach allows measurements in 5 bins in ETand 14 bins in ηwith reasonable statistical precision from a sample of approximately 9000 electrons with the incorrect charge assignment (for – 46 – 2019 JINST 14 P12006 Tight identification). The systematic uncertainty in the parameterization is assessed by comparing, double differentially, the ratio of same-charge events and opposite-charge events, weighted with the charge misreconstruction probability, in data and simulation. The systematic uncertainty is derived by incrementing the uncertainty in steps of 1% until the χ2value falls below 1, separately in each bin in ET. The interactions with material in the inner detector causing electron charge misidentification can also lead to significant energy loss and leakage of energy outside the EM cluster, introducing a correlation between the two effects. In figure 30b, the charge misidentification probability is shown as a function of the energy response, (preco T−ptrue T)/ptrue T, in several bins of η. It increases with the difference between true and reconstructed electron energy. The same effect causes the differences in reconstructed invariant mass between opposite-charge and same-charge events shown in figure 30a. The correlation with the energy response complicates the measurement of charge misidentification probabilities in data. The probability measurement is blind as to which of the two electrons has the incorrect charge assignment. Hence, the probabilities determined from the likelihood maximization are used to form data-to-simulation probability ratios. No significant dependence of the data-to-simulation ratios on the dilepton invariant mass has been observed. The charge misidentification probabilities in data are obtained by multiplying the data-to-simulation probability ratios by the charge misidentification probabilities computed in the simulation, where the electron with the incorrect charge assignment is unambiguous. The probabilities in data are shown in figure 31 for several combinations of identification and isolation operating points. For Medium identification with Tight isolation, the electron charge misidentification probability in Z→ee events is smallest in the central region of the detector at 0.05%, and increases to 2.7% at high |η|. As a function of ETit increases approximately linearly from 0.28% at ET=20 GeV to 1.7% at ET=120 GeV. With Tight instead of Medium identification, a reduction of charge misidentification by 25%–50%, depending on ETand η, is seen. The BDT presented in section 9.1 further reduces the misidentification probability by factor of about five, on average over the detector acceptance, and by up to a factor 10 at high pseudorapidity. 10 Conclusions The reconstruction of electrons and photons based on a dynamical, topological cell clustering algorithm has been described, and the corresponding updates to the methods used for the identification of the candidates and the estimation of their energy have been discussed. The rejection of non-isolated particles and of mismeasured electron candidates have been re-optimized accordingly. The dynamical cell clustering algorithm provides an electron and photon reconstruction efficiency similar to that of the sliding-window reconstruction. A relative improvement of about 15% is obtained in the reconstruction efficiency for two-track photon conversions. The misclassification of unconverted photons as single-track TRT conversions is reduced by a factor of two, while the single-track conversion reconstruction efficiency only decreases by 5 to 10%. The present algorithm also provides a better energy measurement, with a relative improvement in resolution by about 15% in the barrel, and about 20–25% in the endcap, for electrons and converted photons. The resolution for unconverted photons is unchanged. – 47 – 2019 JINST 14 P12006 0 0.01 0.02 0.03 0.04 Data charge misid. probability Medium + Tight isolation Tight + Tight isolation Tight + Tight isolation + BDT ATLAS -1 = 13 TeV, 81 fbs 20 40 60 80 100 120 140 160 180 200 220 240 [GeV] T E 0 0.5 1 1.5 2 Data / MC 0 0.01 0.02 0.03 0.04 Data charge misid. probability Medium + Tight isolation Tight + Tight isolation Tight + Tight isolation + BDT ATLAS -1 = 13 TeV, 81 fbs >20 GeV T E 0 0.5 1 1.5 2 2.5 |η| 0 0.5 1 1.5 2 Data / MC Figure 31. Charge misidentification probabilities in data as a function of ET(left) and |η|(right). The energies of the electrons have been corrected for the energy loss in the interaction with the detector material, which is the primary source of charge misidentification. The inner uncertainties are statistical while the total uncertainties include both the statistical and systematic components. Energy scale and resolution corrections have been measured using electrons from Z→ee decays. A significant dependence of the corrections on the amount of pile-up has been observed, reflecting a mismodelling of the calorimeter activity in minimum-bias events. The uncertainty in the energy scale corrections ranges from 4×10−4in the barrel to 2×10−3in the endcap. The uncertainty in the constant-term resolution corrections is typically 1–2×10−3. The electron-based energy calibration has been verified for photons, using radiative Z-boson decays, to a precision of 0.5% at worst. The identification of electrons and photons has been revisited to match the improved cell clustering procedure. For electrons, identification efficiencies vary from 93% for the Loose identification criterion, to 80% for the Tight criterion, for electrons from Z-boson decays. The simulation models these efficiencies to a precision of 2% for Loose electrons and 5% for Tight electrons, respectively. The efficiency correction factors are measured with a typical precision of 0.2%. In the case of photons, the identification efficiency reaches 92% for unconverted photons, and 98% for converted photons, for ET∼70 GeV and above. The precision of the efficiency correction factors ranges from 7% at low ETto 0.5% at high ETfor unconverted photons, and from 12% to 1% for converted photons. Several electron and photon isolation selection criteria have been defined, targeting a range of processes with varying event activity. The efficiencies of the isolation selections vary from about 99% for the loosest, to about 90% for the tightest criterion, depending on the physics process. Tight isolation selections exhibit a steeply rising efficiency as a function of ET; for all isolation criteria, the selection efficiency varies by about 10% as a function of hµi, for the range of hµispanned by the present dataset. Differences in efficiency between data and simulation range from 1% to 5%, depending on |η|and ET. – 48 – 2019 JINST 14 P12006 A dedicated algorithm has been implemented to reject electrons with badly measured track parameters, with the main objective of reducing the fraction of electron candidates with wrongly measured charge. This fraction, rising from less than 0.1% in the barrel to about 3% at high |η| for all candidates, is reduced by a factor of three to five as a function of ET, and by up to a factor of ten at high |η|. The simulation is found to model the data within 20% for the residual fraction of wrong-charge electron candidates, and the corresponding correction factors are measured with about 50% precision. The present results define the baseline performance of the ATLAS detector for searches and measurements using electrons and photons from LHC proton-proton collision data collected at √s=13TeV. Acknowledgments We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Skłodowska-Curie Actions, European Union; Investissements d’ Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [42]. – 49 – 2019 JINST 14 P12006 References [1] ATLAS collaboration, Measurement of the photon identification efficiencies with the ATLAS detector using LHC Run 2 data collected in 2015 and 2016,Eur. Phys. J. C 79 (2019) 205 [arXiv:1810.05087]. [2] ATLAS collaboration, Electron reconstruction and identification in the ATLAS experiment using the 2015 and 2016 LHC proton-proton collision data at √s=13TeV,Eur. Phys. J. C 79 (2019) 639 [arXiv:1902.04655]. [3] ATLAS collaboration, Electron and photon energy calibration with the ATLAS detector using 2015–2016 LHC proton-proton collision data,2019 JINST 14 P03017 [arXiv:1812.03848]. [4] ATLAS collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1,Eur. Phys. J. C 77 (2017) 490 [arXiv:1603.02934]. [5] ATLAS collaboration, The ATLAS Experiment at the CERN Large Hadron Collider,2008 JINST 3 S08003. [6] ATLAS collaboration, ATLAS Insertable B-Layer Technical Design Report,ATLAS-TDR-19 (2010) [Addendum ATLAS-TDR-19-ADD-1 (2012)]. [7] ATLAS IBL collaboration, Production and integration of the ATLAS Insertable B-Layer,2018 JINST 13 T05008 [arXiv:1803.00844]. [8] ATLAS collaboration, Performance of the ATLAS trigger system in 2015,Eur. Phys. J. C 77 (2017) 317 [arXiv:1611.09661]. [9] ATLAS collaboration, Muon reconstruction performance of the ATLAS detector in proton-proton collision data at √s=13TeV,Eur. Phys. J. C 76 (2016) 292 [arXiv:1603.05598]. [10] S. Alioli, P. Nason, C. Oleari and E. Re, NLO vector-boson production matched with shower in POWHEG,JHEP 07 (2008) 060 [arXiv:0805.4802]. [11] T. Sjöstrand, S. Mrenna and P.Z. Skands, A brief introduction to PYTHIA 8.1,Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820]. [12] H.-L. Lai et al., New parton distributions for collider physics,Phys. Rev. D 82 (2010) 074024 [arXiv:1007.2241]. [13] ATLAS collaboration, Measurement of the Z/γ∗boson transverse momentum distribution in pp collisions at √s=7TeV with the ATLAS detector,JHEP 09 (2014) 145 [arXiv:1406.3660]. [14] J. Pumplin, D.R. Stump, J. Huston, H.-L. Lai, P.M. Nadolsky and W.K. Tung, New generation of parton distributions with uncertainties from global QCD analysis,JHEP 07 (2002) 012 [hep-ph/0201195]. [15] N. Davidson, T. Przedzinski and Z. Was, PHOTOS interface in C++: Technical and physics documentation,Comput. Phys. Commun. 199 (2016) 86 [arXiv:1011.0937]. [16] T. Gleisberg et al., Event generation with SHERPA 1.1,JHEP 02 (2009) 007 [arXiv:0811.4622]. [17] NNPDF collaboration, Parton distributions for the LHC Run II,JHEP 04 (2015) 040 [arXiv:1410.8849]. [18] ATLAS collaboration, ATLAS Pythia 8 tunes to 7TeV data,ATL-PHYS-PUB-2014-021 (2014). [19] R.D. Ball et al., Parton distributions with LHC data,Nucl. Phys. B 867 (2013) 244 [arXiv:1207.1303]. [20] D.J. Lange, The EvtGen particle decay simulation package,Nucl. Instrum. Meth. A 462 (2001) 152. – 50 – 2019 JINST 14 P12006 [21] ATLAS collaboration, The ATLAS Simulation Infrastructure,Eur. Phys. J. C 70 (2010) 823 [arXiv:1005.4568]. [22] GEANT4 collaboration, GEANT4 — a simulation toolkit,Nucl. Instrum. Meth. A 506 (2003) 250. [23] ATLAS collaboration, The Pythia 8 A3 tune description of ATLAS minimum bias and inelastic measurements incorporating the Donnachie-Landshoff diffractive model,ATL-PHYS-PUB-2016-017 (2016). [24] ATLAS collaboration, Summary of ATLAS PYTHIA 8 tunes,ATL-PHYS-PUB-2012-003 (2012). [25] ATLAS collaboration, Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2,Eur. Phys. J. C 77 (2017) 673 [arXiv:1704.07983]. [26] W. Lampl et al., Calorimeter Clustering Algorithms: Description and Performance, ATL-LARG-PUB-2008-002 (2008). [27] T. Cornelissen, M. Elsing, S. Fleischmann, W. Liebig, E. Moyse and A. Salzburger, Concepts, Design and Implementation of the ATLAS New Tracking (NEWT),ATL-SOFT-PUB-2007-007 (2007). [28] R. Frühwirth, Application of Kalman filtering to track and vertex fitting,Nucl. Instrum. Meth. A 262 (1987) 444. [29] T.G. Cornelissen et al., The global χ2track fitter in ATLAS,J. Phys. Conf. Ser. 119 (2008) 032013. [30] ATLAS collaboration, Improved electron reconstruction in ATLAS using the Gaussian Sum Filter-based model for bremsstrahlung,ATLAS-CONF-2012-047 (2012). [31] ATLAS collaboration, Particle Identification Performance of the ATLAS Transition Radiation Tracker,ATLAS-CONF-2011-128 (2011). [32] ATLAS collaboration, Performance of the ATLAS Transition Radiation Tracker in Run 1 of the LHC: tracker properties,2017 JINST 12 P05002 [arXiv:1702.06473]. [33] C. de La Taille and L. Serin, Temperature dependance of the ATLAS electromagnetic calorimeter signal. Preliminary drift time measurement,ATL-LARG-95-029 (1995). [34] ATLAS collaboration, Study of the material of the ATLAS inner detector for Run 2 of the LHC,2017 JINST 12 P12009 [arXiv:1707.02826]. [35] D.W. Scott, Multivariate Density Estimation, Theory, Practice, and Visualization, Wiley-Interscience, New York U.S.A. (1992). [36] A. Hocker et al., TMVA 4. Toolkit for Multivariate Data Analysis with ROOT. Users Guide, physics/0703039. [37] ATLAS collaboration, Electron efficiency measurements with the ATLAS detector using 2012 LHC proton-proton collision data,Eur. Phys. J. C 77 (2017) 195 [arXiv:1612.01456]. [38] L. Devroye, Non-Uniform Random Variate Generation, Springer-Verlag (1986). [39] M. Cacciari and G.P. Salam, Pileup subtraction using jet areas,Phys. Lett. B 659 (2008) 119 [arXiv:0707.1378]. [40] ATLAS collaboration, Electron performance measurements with the ATLAS detector using the 2010 LHC proton-proton collision data,Eur. Phys. J. C 72 (2012) 1909 [arXiv:1110.3174]. [41] M. Oreglia, A Study of the Reactions ψ0→γγψ, Ph.D. Thesis, Stanford University, Stanford California U.S.A. (1980) [SLAC-R-236] and online pdf version at https://www-public.slac.stanford.edu/sciDoc/docMeta.aspx?slacPubNumber=slac-R-236. [42] ATLAS collaboration, ATLAS Computing Acknowledgements,ATL-GEN-PUB-2016-002 (2016). – 51 – 2019 JINST 14 P12006 The ATLAS collaboration G. Aad101, B. Abbott128, D.C. Abbott102, A. Abed Abud70a,70b, K. Abeling53, D.K. Abhayasinghe93, S.H. Abidi167, O.S. AbouZeid40, N.L. Abraham156, H. Abramowicz161, H. Abreu160, Y. Abulaiti6, B.S. Acharya66a,66b,o, B. Achkar53, S. Adachi163, L. Adam99, C. Adam Bourdarios5, L. Adamczyk83a, L. Adamek167, J. Adelman121, M. Adersberger114, A. Adiguzel12c,aj, S. Adorni54, T. Adye144, A.A. Affolder146, Y. Afik160, C. Agapopoulou132, M.N. Agaras38, A. Aggarwal119, C. Agheorghiesei27c, J.A. Aguilar-Saavedra140f,140a,ai, F. Ahmadov79, W.S. Ahmed103, X. Ai18, G. Aielli73a,73b, S. Akatsuka85, T.P.A. Åkesson96, E. Akilli54, A.V. Akimov110, K. Al Khoury132, G.L. Alberghi23b,23a, J. Albert176, M.J. Alconada Verzini161, S. Alderweireldt36, M. Aleksa36, I.N. Aleksandrov79, C. Alexa27b, D. Alexandre19, T. Alexopoulos10, A. Alfonsi120, F. Alfonsi23b,23a, M. Alhroob128, B. Ali142, G. Alimonti68a, J. Alison37, S.P. Alkire148, C. Allaire132, B.M.M. Allbrooke156, B.W. Allen131, P.P. Allport21, A. Aloisio69a,69b, A. Alonso40, F. Alonso88, C. Alpigiani148, A.A. Alshehri57, M. Alvarez Estevez98, D. Álvarez Piqueras174, M.G. Alviggi69a,69b, Y. Amaral Coutinho80b, A. Ambler103, L. Ambroz135, C. Amelung26, D. Amidei105, S.P. Amor Dos Santos140a, S. Amoroso46, C.S. Amrouche54, F. An78, C. Anastopoulos149, N. Andari145, T. Andeen11, C.F. Anders61b, J.K. Anders20, A. Andreazza68a,68b, V. Andrei61a, C.R. Anelli176, S. Angelidakis38, A. Angerami39, A.V. Anisenkov122b,122a, A. Annovi71a, C. Antel61a, M.T. Anthony149, M. Antonelli51, D.J.A. Antrim171, F. Anulli72a, M. Aoki81, J.A. Aparisi Pozo174, L. Aperio Bella15a, G. Arabidze106, J.P. Araque140a, V. Araujo Ferraz80b, R. Araujo Pereira80b, C. Arcangeletti51, A.T.H. Arce49, F.A. Arduh88, J-F. Arguin109, S. Argyropoulos77, J.-H. Arling46, A.J. Armbruster36, A. Armstrong171, O. Arnaez167, H. Arnold120, Z.P. Arrubarrena Tame114, A. Artamonov111,∗, G. Artoni135, S. Artz99, S. Asai163, N. Asbah59, E.M. Asimakopoulou172, L. Asquith156, J. Assahsah35d, K. Assamagan29, R. Astalos28a, R.J. Atkin33a, M. Atkinson173, N.B. Atlay19, H. Atmani132, K. Augsten142, G. Avolio36, R. Avramidou60a, M.K. Ayoub15a, A.M. Azoulay168b, G. Azuelos109,ay, H. Bachacou145, K. Bachas67a,67b, M. Backes135, F. Backman45a,45b, P. Bagnaia72a,72b, M. Bahmani84, H. Bahrasemani152, A.J. Bailey174, V.R. Bailey173, J.T. Baines144, M. Bajic40, C. Bakalis10, O.K. Baker183, P.J. Bakker120, D. Bakshi Gupta8, S. Balaji157, E.M. Baldin122b,122a, P. Balek180, F. Balli145, W.K. Balunas135, J. Balz99, E. Banas84, A. Bandyopadhyay24, Sw. Banerjee181,j, A.A.E. Bannoura182, L. Barak161, W.M. Barbe38, E.L. Barberio104, D. Barberis55b,55a, M. Barbero101, G. Barbour94, T. Barillari115, M-S. Barisits36, J. Barkeloo131, T. Barklow153, R. Barnea160, S.L. Barnes60c, B.M. Barnett144, R.M. Barnett18, Z. Barnovska-Blenessy60a, A. Baroncelli60a, G. Barone29, A.J. Barr135, L. Barranco Navarro45a,45b, F. Barreiro98, J. Barreiro Guimarães da Costa15a, S. Barsov138, R. Bartoldus153, G. Bartolini101, A.E. Barton89, P. Bartos28a, A. Basalaev46, A. Bassalat132,ar, M.J. Basso167, R.L. Bates57, S. Batlamous35e, J.R. Batley32, B. Batool151, M. Battaglia146, M. Bauce72a,72b, F. Bauer145, K.T. Bauer171, H.S. Bawa31,m, J.B. Beacham49, T. Beau136, P.H. Beauchemin170, F. Becherer52, P. Bechtle24, H.C. Beck53, H.P. Beck20,s, K. Becker52, M. Becker99, C. Becot46, A. Beddall12d, A.J. Beddall12a, V.A. Bednyakov79, M. Bedognetti120, C.P. Bee155, T.A. Beermann76, M. Begalli80b, M. Begel29, A. Behera155, J.K. Behr46, F. Beisiegel24, A.S. Bell94, G. Bella161, L. Bellagamba23b, A. Bellerive34, P. Bellos9, K. Beloborodov122b,122a, K. Belotskiy112, N.L. Belyaev112, D. Benchekroun35a, N. Benekos10, Y. Benhammou161, D.P. Benjamin6, M. Benoit54, J.R. Bensinger26, S. Bentvelsen120, L. Beresford135, M. Beretta51, D. Berge46, E. Bergeaas Kuutmann172, N. Berger5, B. Bergmann142, L.J. Bergsten26, J. Beringer18, S. Berlendis7, N.R. Bernard102, G. Bernardi136, C. Bernius153, T. Berry93, P. Berta99, C. Bertella15a, I.A. Bertram89, O. Bessidskaia Bylund182, N. Besson145, A. Bethani100, S. Bethke115, A. Betti24, A.J. Bevan92, J. Beyer115, D.S. Bhattacharya177, R. Bi139, R.M. Bianchi139, O. Biebel114, D. Biedermann19, R. Bielski36, K. Bierwagen99, N.V. Biesuz71a,71b, M. Biglietti74a, T.R.V. Billoud109, M. Bindi53, A. Bingul12d, C. Bini72a,72b, S. Biondi23b,23a, M. Birman180, T. Bisanz53, J.P. Biswal161, D. Biswas181,j, A. Bitadze100, C. Bittrich48, K. Bjørke134, K.M. Black25, T. Blazek28a, I. Bloch46, C. Blocker26, A. Blue57, U. Blumenschein92, G.J. Bobbink120, V.S. Bobrovnikov122b,122a, – 52 – 2019 JINST 14 P12006 S.S. Bocchetta96, A. Bocci49, D. Boerner46, D. Bogavac14, A.G. Bogdanchikov122b,122a, C. Bohm45a, V. Boisvert93, P. Bokan53,172, T. Bold83a, A.S. Boldyrev113, A.E. Bolz61b, M. Bomben136, M. Bona92, J.S. Bonilla131, M. Boonekamp145, H.M. Borecka-Bielska90, A. Borisov123, G. Borissov89, J. Bortfeldt36, D. Bortoletto135, D. Boscherini23b, M. Bosman14, J.D. Bossio Sola103, K. Bouaouda35a, J. Boudreau139, E.V. Bouhova-Thacker89, D. Boumediene38, S.K. Boutle57, A. Boveia126, J. Boyd36, D. Boye33b,as, I.R. Boyko79, A.J. Bozson93, J. Bracinik21, N. Brahimi101, G. Brandt182, O. Brandt32, F. Braren46, B. Brau102, J.E. Brau131, W.D. Breaden Madden57, K. Brendlinger46, L. Brenner46, R. Brenner172, S. Bressler180, B. Brickwedde99, D.L. Briglin21, D. Britton57, D. Britzger115, I. Brock24, R. Brock106, G. Brooijmans39, W.K. Brooks147b, E. Brost121, J.H Broughton21, P.A. Bruckman de Renstrom84, D. Bruncko28b, A. Bruni23b, G. Bruni23b, L.S. Bruni120, S. Bruno73a,73b, B.H. Brunt32, M. Bruschi23b, N. Bruscino139, P. Bryant37, L. Bryngemark96, T. Buanes17, Q. Buat36, P. Buchholz151, A.G. Buckley57, I.A. Budagov79, M.K. Bugge134, F. Bührer52, O. Bulekov112, T.J. Burch121, S. Burdin90, C.D. Burgard120, A.M. Burger129, B. Burghgrave8, J.T.P. Burr46, C.D. Burton11, J.C. Burzynski102, V. Büscher99, E. Buschmann53, P.J. Bussey57, J.M. Butler25, C.M. Buttar57, J.M. Butterworth94, P. Butti36, W. Buttinger36, A. Buzatu158, A.R. Buzykaev122b,122a, G. Cabras23b,23a, S. Cabrera Urbán174, D. Caforio56, H. Cai173, V.M.M. Cairo153, O. Cakir4a, N. Calace36, P. Calafiura18, A. Calandri101, G. Calderini136, P. Calfayan65, G. Callea57, L.P. Caloba80b, S. Calvente Lopez98, D. Calvet38, S. Calvet38, T.P. Calvet155, M. Calvetti71a,71b, R. Camacho Toro136, S. Camarda36, D. Camarero Munoz98, P. Camarri73a,73b, D. Cameron134, R. Caminal Armadans102, C. Camincher36, S. Campana36, M. Campanelli94, A. Camplani40, A. Campoverde151, V. Canale69a,69b, A. Canesse103, M. Cano Bret60c, J. Cantero129, T. Cao161, Y. Cao173, M.D.M. Capeans Garrido36, M. Capua41b,41a, R. Cardarelli73a, F. Cardillo149, G. Carducci41b,41a, I. Carli143, T. Carli36, G. Carlino69a, B.T. Carlson139, L. Carminati68a,68b, R.M.D. Carney45a,45b, S. Caron119, E. Carquin147b, S. Carrá46, J.W.S. Carter167, M.P. Casado14,e, A.F. Casha167, D.W. Casper171, R. Castelijn120, F.L. Castillo174, V. Castillo Gimenez174, N.F. Castro140a,140e, A. Catinaccio36, J.R. Catmore134, A. Cattai36, J. Caudron24, V. Cavaliere29, E. Cavallaro14, M. Cavalli-Sforza14, V. Cavasinni71a,71b, E. Celebi12b, F. Ceradini74a,74b, L. Cerda Alberich174, K. Cerny130, A.S. Cerqueira80a, A. Cerri156, L. Cerrito73a,73b, F. Cerutti18, A. Cervelli23b,23a, S.A. Cetin12b, Z. Chadi35a, D. Chakraborty121, S.K. Chan59, W.S. Chan120, W.Y. Chan90, J.D. Chapman32, B. Chargeishvili159b, D.G. Charlton21, T.P. Charman92, C.C. Chau34, S. Che126, S. Chekanov6, S.V. Chekulaev168a, G.A. Chelkov79,ax, M.A. Chelstowska36, B. Chen78, C. Chen60a, C.H. Chen78, H. Chen29, J. Chen60a, J. Chen39, S. Chen137, S.J. Chen15c, X. Chen15b,aw, Y. Chen82, Y-H. Chen46, H.C. Cheng63a, H.J. Cheng15a,15d, A. Cheplakov79, E. Cheremushkina123, R. Cherkaoui El Moursli35e, E. Cheu7, K. Cheung64, T.J.A. Chevalérias145, L. Chevalier145, V. Chiarella51, G. Chiarelli71a, G. Chiodini67a, A.S. Chisholm21, A. Chitan27b, I. Chiu163, Y.H. Chiu176, M.V. Chizhov79, K. Choi65, A.R. Chomont72a,72b, S. Chouridou162, Y.S. Chow120, M.C. Chu63a, X. Chu15a, J. Chudoba141, A.J. Chuinard103, J.J. Chwastowski84, L. Chytka130, D. Cieri115, K.M. Ciesla84, D. Cinca47, V. Cindro91, I.A. Cioară27b, A. Ciocio18, F. Cirotto69a,69b, Z.H. Citron180,k, M. Citterio68a, D.A. Ciubotaru27b, B.M. Ciungu167, A. Clark54, M.R. Clark39, P.J. Clark50, C. Clement45a,45b, Y. Coadou101, M. Cobal66a,66c, A. Coccaro55b, J. Cochran78, H. Cohen161, A.E.C. Coimbra36, L. Colasurdo119, B. Cole39, A.P. Colijn120, J. Collot58, P. Conde Muiño140a,f, E. Coniavitis52, S.H. Connell33b, I.A. Connelly57, S. Constantinescu27b, F. Conventi69a,az, A.M. Cooper-Sarkar135, F. Cormier175, K.J.R. Cormier167, L.D. Corpe94, M. Corradi72a,72b, E.E. Corrigan96, F. Corriveau103,ae, A. Cortes-Gonzalez36, M.J. Costa174, F. Costanza5, D. Costanzo149, G. Cowan93, J.W. Cowley32, J. Crane100, K. Cranmer124, S.J. Crawley57, R.A. Creager137, S. Crépé-Renaudin58, F. Crescioli136, M. Cristinziani24, V. Croft120, G. Crosetti41b,41a, A. Cueto5, T. Cuhadar Donszelmann149, A.R. Cukierman153, S. Czekierda84, P. Czodrowski36, M.J. Da Cunha Sargedas De Sousa60b, J.V. Da Fonseca Pinto80b, C. Da Via100, W. Dabrowski83a, T. Dado28a, S. Dahbi35e, T. Dai105, C. Dallapiccola102, M. Dam40, G. D’amen29, V. D’Amico74a,74b, J. Damp99, J.R. Dandoy137, M.F. Daneri30, N.P. Dang181,j, N.S. Dann100, M. Danninger175, V. Dao36, G. Darbo55b, – 53 – 2019 JINST 14 P12006 O. Dartsi5, A. Dattagupta131, T. Daubney46, S. D’Auria68a,68b, W. Davey24, C. David46, T. Davidek143, D.R. Davis49, I. Dawson149, K. De8, R. De Asmundis69a, M. De Beurs120, S. De Castro23b,23a, S. De Cecco72a,72b, N. De Groot119, P. de Jong120, H. De la Torre106, A. De Maria15c, D. De Pedis72a, A. De Salvo72a, U. De Sanctis73a,73b, M. De Santis73a,73b, A. De Santo156, K. De Vasconcelos Corga101, J.B. De Vivie De Regie132, C. Debenedetti146, D.V. Dedovich79, A.M. Deiana42, M. Del Gaudio41b,41a, J. Del Peso98, Y. Delabat Diaz46, D. Delgove132, F. Deliot145,r, C.M. Delitzsch7, M. Della Pietra69a,69b, D. Della Volpe54, A. Dell’Acqua36, L. Dell’Asta73a,73b, M. Delmastro5, C. Delporte132, P.A. Delsart58, D.A. DeMarco167, S. Demers183, M. Demichev79, G. Demontigny109, S.P. Denisov123, D. Denysiuk120, L. D’Eramo136, D. Derendarz84, J.E. Derkaoui35d, F. Derue136, P. Dervan90, K. Desch24, C. Deterre46, K. Dette167, C. Deutsch24, M.R. Devesa30, P.O. Deviveiros36, A. Dewhurst144, F.A. Di Bello54, A. Di Ciaccio73a,73b, L. Di Ciaccio5, W.K. Di Clemente137, C. Di Donato69a,69b, A. Di Girolamo36, G. Di Gregorio71a,71b, B. Di Micco74a,74b, R. Di Nardo102, K.F. Di Petrillo59, R. Di Sipio167, D. Di Valentino34, C. Diaconu101, F.A. Dias40, T. Dias Do Vale140a, M.A. Diaz147a, J. Dickinson18, E.B. Diehl105, J. Dietrich19, S. Díez Cornell46, A. Dimitrievska18, W. Ding15b, J. Dingfelder24, F. Dittus36, F. Djama101, T. Djobava159b, J.I. Djuvsland17, M.A.B. Do Vale80c, M. Dobre27b, D. Dodsworth26, C. Doglioni96, J. Dolejsi143, Z. Dolezal143, M. Donadelli80d, B. Dong60c, J. Donini38, A. D’onofrio92, M. D’Onofrio90, J. Dopke144, A. Doria69a, M.T. Dova88, A.T. Doyle57, E. Drechsler152, E. Dreyer152, T. Dreyer53, A.S. Drobac170, Y. Duan60b, F. Dubinin110, M. Dubovsky28a, A. Dubreuil54, E. Duchovni180, G. Duckeck114, A. Ducourthial136, O.A. Ducu109, D. Duda115, A. Dudarev36, A.C. Dudder99, E.M. Duffield18, L. Duflot132, M. Dührssen36, C. Dülsen182, M. Dumancic180, A.E. Dumitriu27b, A.K. Duncan57, M. Dunford61a, A. Duperrin101, H. Duran Yildiz4a, M. Düren56, A. Durglishvili159b, D. Duschinger48, B. Dutta46, D. Duvnjak1, G.I. Dyckes137, M. Dyndal36, S. Dysch100, B.S. Dziedzic84, K.M. Ecker115, R.C. Edgar105, M.G. Eggleston49, T. Eifert36, G. Eigen17, K. Einsweiler18, T. Ekelof172, H. El Jarrari35e, M. El Kacimi35c, R. El Kosseifi101, V. Ellajosyula172, M. Ellert172, F. Ellinghaus182, A.A. Elliot92, N. Ellis36, J. Elmsheuser29, M. Elsing36, D. Emeliyanov144, A. Emerman39, Y. Enari163, M.B. Epland49, J. Erdmann47, A. Ereditato20, M. Errenst36, M. Escalier132, C. Escobar174, O. Estrada Pastor174, E. Etzion161, H. Evans65, A. Ezhilov138, F. Fabbri57, L. Fabbri23b,23a, V. Fabiani119, G. Facini94, R.M. Faisca Rodrigues Pereira140a, R.M. Fakhrutdinov123, S. Falciano72a, P.J. Falke5, S. Falke5, J. Faltova143, Y. Fang15a, Y. Fang15a, G. Fanourakis44, M. Fanti68a,68b, M. Faraj66a,66c,u, A. Farbin8, A. Farilla74a, E.M. Farina70a,70b, T. Farooque106, S. Farrell18, S.M. Farrington50, P. Farthouat36, F. Fassi35e, P. Fassnacht36, D. Fassouliotis9, M. Faucci Giannelli50, W.J. Fawcett32, L. Fayard132, O.L. Fedin138,p, W. Fedorko175, M. Feickert42, L. Feligioni101, A. Fell149, C. Feng60b, E.J. Feng36, M. Feng49, M.J. Fenton57, A.B. Fenyuk123, J. Ferrando46, A. Ferrante173, A. Ferrari172, P. Ferrari120, R. Ferrari70a, D.E. Ferreira de Lima61b, A. Ferrer174, D. Ferrere54, C. Ferretti105, F. Fiedler99, A. Filipčič91, F. Filthaut119, K.D. Finelli25, M.C.N. Fiolhais140a,140c,a, L. Fiorini174, F. Fischer114, W.C. Fisher106, I. Fleck151, P. Fleischmann105, R.R.M. Fletcher137, T. Flick182, B.M. Flierl114, L. Flores137, L.R. Flores Castillo63a, F.M. Follega75a,75b, N. Fomin17, J.H. Foo167, G.T. Forcolin75a,75b, A. Formica145, F.A. Förster14, A.C. Forti100, A.G. Foster21, M.G. Foti135, D. Fournier132, H. Fox89, P. Francavilla71a,71b, S. Francescato72a,72b, M. Franchini23b,23a, S. Franchino61a, D. Francis36, L. Franconi20, M. Franklin59, A.N. Fray92, P.M. Freeman21, B. Freund109, W.S. Freund80b, E.M. Freundlich47, D.C. Frizzell128, D. Froidevaux36, J.A. Frost135, C. Fukunaga164, E. Fullana Torregrosa174, E. Fumagalli55b,55a, T. Fusayasu116, J. Fuster174, A. Gabrielli23b,23a, A. Gabrielli18, G.P. Gach83a, S. Gadatsch54, P. Gadow115, G. Gagliardi55b,55a, L.G. Gagnon109, C. Galea27b, B. Galhardo140a, G.E. Gallardo135, E.J. Gallas135, B.J. Gallop144, G. Galster40, R. Gamboa Goni92, K.K. Gan126, S. Ganguly180, J. Gao60a, Y. Gao50, Y.S. Gao31,m, C. García174, J.E. García Navarro174, J.A. García Pascual15a, C. Garcia-Argos52, M. Garcia-Sciveres18, R.W. Gardner37, N. Garelli153, S. Gargiulo52, V. Garonne134, A. Gaudiello55b,55a, G. Gaudio70a, I.L. Gavrilenko110, A. Gavrilyuk111, C. Gay175, G. Gaycken46, E.N. Gazis10, A.A. Geanta27b, C.M. Gee146, C.N.P. Gee144, J. Geisen53, M. Geisen99, M.P. Geisler61a, C. Gemme55b, M.H. Genest58, – 54 – 2019 JINST 14 P12006 M. Vogel182, P. Vokac142, S.E. von Buddenbrock33c, E. Von Toerne24, V. Vorobel143, K. Vorobev112, M. Vos174, J.H. Vossebeld90, M. Vozak100, N. Vranjes16, M. Vranjes Milosavljevic16, V. Vrba142, M. Vreeswijk120, R. Vuillermet36, I. Vukotic37, P. Wagner24, W. Wagner182, J. Wagner-Kuhr114, S. Wahdan182, H. Wahlberg88, V.M. Walbrecht115, J. Walder89, R. Walker114, S.D. Walker93, W. Walkowiak151, V. Wallangen45a,45b, A.M. Wang59, C. Wang60c, C. Wang60b, F. Wang181, H. Wang18, H. Wang3, J. Wang157, J. Wang61b, P. Wang42, Q. Wang128, R.-J. Wang99, R. Wang60a, R. Wang6, S.M. Wang158, W.T. Wang60a, W. Wang15c,af, W.X. Wang60a,af, Y. Wang60a,an, Z. Wang60c, C. Wanotayaroj46, A. Warburton103, C.P. Ward32, D.R. Wardrope94, N. Warrack57, A. Washbrook50, A.T. Watson21, M.F. Watson21, G. Watts148, B.M. Waugh94, A.F. Webb11, S. Webb99, C. Weber183, M.S. Weber20, S.A. Weber34, S.M. Weber61a, A.R. Weidberg135, J. Weingarten47, M. Weirich99, C. Weiser52, P.S. Wells36, T. Wenaus29, T. Wengler36, S. Wenig36, N. Wermes24, M.D. Werner78, M. Wessels61a, T.D. Weston20, K. Whalen131, N.L. Whallon148, A.M. Wharton89, A.S. White105, A. White8, M.J. White1, D. Whiteson171, B.W. Whitmore89, W. Wiedenmann181, M. Wielers144, N. Wieseotte99, C. Wiglesworth40, L.A.M. Wiik-Fuchs52, F. Wilk100, H.G. Wilkens36, L.J. Wilkins93, H.H. Williams137, S. Williams32, C. Willis106, S. Willocq102, J.A. Wilson21, I. Wingerter-Seez5, E. Winkels156, F. Winklmeier131, O.J. Winston156, B.T. Winter52, M. Wittgen153, M. Wobisch95, A. Wolf99, T.M.H. Wolf120, R. Wolff101, R.W. Wölker135, J. Wollrath52, M.W. Wolter84, H. Wolters140a,140c, V.W.S. Wong175, N.L. Woods146, S.D. Worm21, B.K. Wosiek84, K.W. Woźniak84, K. Wraight57, S.L. Wu181, X. Wu54, Y. Wu60a, T.R. Wyatt100, B.M. Wynne50, S. Xella40, Z. Xi105, L. Xia178, X. Xiao105, I. Xiotidis156, D. Xu15a, H. Xu60a,c, L. Xu29, T. Xu145, W. Xu105, Z. Xu60b, Z. Xu153, B. Yabsley157, S. Yacoob33a, K. Yajima133, D.P. Yallup94, D. Yamaguchi165, Y. Yamaguchi165, A. Yamamoto81, M. Yamatani163, T. Yamazaki163, Y. Yamazaki82, Z. Yan25, H.J. Yang60c,60d, H.T. Yang18, S. Yang77, X. Yang60b,58, Y. Yang163, W-M. Yao18, Y.C. Yap46, Y. Yasu81, E. Yatsenko60c,60d, J. Ye42, S. Ye29, I. Yeletskikh79, M.R. Yexley89, E. Yigitbasi25, K. Yorita179, K. Yoshihara137, C.J.S. Young36, C. Young153, J. Yu78, R. Yuan60b,i, X. Yue61a, S.P.Y. Yuen24, M. Zaazoua35e, B. Zabinski84, G. Zacharis10, E. Zaffaroni54, J. Zahreddine136, A.M. Zaitsev123,ap, T. Zakareishvili159b, N. Zakharchuk34, S. Zambito59, D. Zanzi36, D.R. Zaripovas57, S.V. Zeißner47, C. Zeitnitz182, G. Zemaityte135, J.C. Zeng173, O. Zenin123, T. Ženiš28a, D. Zerwas132, M. Zgubič135, D.F. Zhang15b, G. Zhang15b, H. Zhang15c, J. Zhang6, L. Zhang15c, L. Zhang60a, M. Zhang173, R. Zhang24, X. Zhang60b, Y. Zhang15a,15d, Z. Zhang63a, Z. Zhang132, P. Zhao49, Y. Zhao60b, Z. Zhao60a, A. Zhemchugov79, Z. Zheng105, D. Zhong173, B. Zhou105, C. Zhou181, M.S. Zhou15a,15d, M. Zhou155, N. Zhou60c, Y. Zhou7, C.G. Zhu60b, H.L. Zhu60a, H. Zhu15a, J. Zhu105, Y. Zhu60a, X. Zhuang15a, K. Zhukov110, V. Zhulanov122b,122a, D. Zieminska65, N.I. Zimine79, S. Zimmermann52, Z. Zinonos115, M. Ziolkowski151, L. Živković16, G. Zobernig181, A. Zoccoli23b,23a, K. Zoch53, T.G. Zorbas149, R. Zou37, L. Zwalinski36 1Department of Physics, University of Adelaide, Adelaide, Australia 2Physics Department, SUNY Albany, Albany NY, United States of America 3Department of Physics, University of Alberta, Edmonton AB, Canada 4(a)Department of Physics, Ankara University, Ankara; (b)Istanbul Aydin University, Istanbul; (c)Division of Physics, TOBB University of Economics and Technology, Ankara, Turkey 5LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL, United States of America 7Department of Physics, University of Arizona, Tucson AZ, United States of America 8Department of Physics, University of Texas at Arlington, Arlington TX, United States of America 9Physics Department, National and Kapodistrian University of Athens, Athens, Greece 10 Physics Department, National Technical University of Athens, Zografou, Greece 11 Department of Physics, University of Texas at Austin, Austin TX, United States of America 12 (a)Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; (b)Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul; (c)Department of Physics, Bogazici University, Istanbul; (d)Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey – 61 – 2019 JINST 14 P12006 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; (b)Physics Department, Tsinghua University, Beijing; (c)Department of Physics, Nanjing University, Nanjing; (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, United States of America 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 22 Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogota, Colombia 23 (a)INFN Bologna and Universita’ di Bologna, Dipartimento di Fisica; (b)INFN Sezione di Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston MA, United States of America 26 Department of Physics, Brandeis University, Waltham MA, United States of America 27 (a)Transilvania University of Brasov, Brasov; (b)Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest; (c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi; (d)National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca; (e)University Politehnica Bucharest, Bucharest; (f)West University in Timisoara, Timisoara, Romania 28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; (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, United States of America 30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 31 California State University, CA, United States of America 32 Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 33 (a)Department of Physics, University of Cape Town, Cape Town; (b)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg; (c)School of Physics, University of the Witwatersrand, Johannesburg, South Africa 34 Department of Physics, Carleton University, Ottawa ON, Canada 35 (a)Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies — Université Hassan II, Casablanca; (b)Faculté des Sciences, Université Ibn-Tofail, Kénitra; (c)Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech; (d)Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda; (e)Faculté des sciences, Université Mohammed V, Rabat, Morocco 36 CERN, Geneva, Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL, United States of America 38 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 39 Nevis Laboratory, Columbia University, Irvington NY, United States of America 40 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 41 (a)Dipartimento di Fisica, Università della Calabria, Rende; (b)INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Italy 42 Physics Department, Southern Methodist University, Dallas TX, United States of America 43 Physics Department, University of Texas at Dallas, Richardson TX, United States of America 44 National Centre for Scientific Research “Demokritos”, Agia Paraskevi, Greece 45 (a)Department of Physics, Stockholm University; (b)Oskar Klein Centre, Stockholm, Sweden 46 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 47 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 48 Institut für Kern und Teilchenphysik, Technische Universität Dresden, Dresden, Germany – 62 – 2019 JINST 14 P12006 49 Department of Physics, Duke University, Durham NC, United States of America 50 SUPA — School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 51 INFN e Laboratori Nazionali di Frascati, Frascati, Italy 52 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 53 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 54 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland 55 (a)Dipartimento di Fisica, Università di Genova, Genova; (b)INFN Sezione di Genova, Italy 56 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 57 SUPA — School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 58 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 59 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA, United States of America 60 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei; (b)Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao; (c)School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai; (d)Tsung-Dao Lee Institute, Shanghai, China 61 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; (b)Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 62 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 63 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b)Department of Physics, University of Hong Kong, Hong Kong; (c)Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 64 Department of Physics, National Tsing Hua University, Hsinchu, Taiwan 65 Department of Physics, Indiana University, Bloomington IN, United States of America 66 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; (b)ICTP, Trieste; (c)Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Udine, Italy 67 (a)INFN Sezione di Lecce; (b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 68 (a)INFN Sezione di Milano; (b)Dipartimento di Fisica, Università di Milano, Milano, Italy 69 (a)INFN Sezione di Napoli; (b)Dipartimento di Fisica, Università di Napoli, Napoli, Italy 70 (a)INFN Sezione di Pavia; (b)Dipartimento di Fisica, Università di Pavia, Pavia, Italy 71 (a)INFN Sezione di Pisa; (b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 72 (a)INFN Sezione di Roma; (b)Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy 73 (a)INFN Sezione di Roma Tor Vergata; (b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma, Italy 74 (a)INFN Sezione di Roma Tre; (b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma, Italy 75 (a)INFN-TIFPA; (b)Università degli Studi di Trento, Trento, Italy 76 Institut für Astro und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 77 University of Iowa, Iowa City IA, United States of America 78 Department of Physics and Astronomy, Iowa State University, Ames IA, United States of America 79 Joint Institute for Nuclear Research, Dubna, Russia 80 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora; (b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; (c)Universidade Federal de São João del Rei (UFSJ), São João del Rei; (d)Instituto de Física, Universidade de São Paulo, São Paulo, Brazil 81 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 82 Graduate School of Science, Kobe University, Kobe, Japan 83 (a)AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow; (b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland 84 Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland 85 Faculty of Science, Kyoto University, Kyoto, Japan 86 Kyoto University of Education, Kyoto, Japan 87 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka , Japan 88 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 89 Physics Department, Lancaster University, Lancaster, United Kingdom – 63 – 2019 JINST 14 P12006 90 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 91 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana, Slovenia 92 School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom 93 Department of Physics, Royal Holloway University of London, Egham, United Kingdom 94 Department of Physics and Astronomy, University College London, London, United Kingdom 95 Louisiana Tech University, Ruston LA, United States of America 96 Fysiska institutionen, Lunds universitet, Lund, Sweden 97 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne, France 98 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 99 Institut für Physik, Universität Mainz, Mainz, Germany 100 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 101 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 102 Department of Physics, University of Massachusetts, Amherst MA, United States of America 103 Department of Physics, McGill University, Montreal QC, Canada 104 School of Physics, University of Melbourne, Victoria, Australia 105 Department of Physics, University of Michigan, Ann Arbor MI, United States of America 106 Department of Physics and Astronomy, Michigan State University, East Lansing MI, United States of America 107 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus 108 Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus 109 Group of Particle Physics, University of Montreal, Montreal QC, Canada 110 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 111 Institute for Theoretical and Experimental Physics of the National Research Centre Kurchatov Institute, Moscow, Russia 112 National Research Nuclear University MEPhI, Moscow, Russia 113 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 114 Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany 115 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany 116 Nagasaki Institute of Applied Science, Nagasaki, Japan 117 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 118 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM, United States of America 119 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands 120 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands 121 Department of Physics, Northern Illinois University, DeKalb IL, United States of America 122 (a)Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk; (b)Novosibirsk State University Novosibirsk, Russia 123 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino, Russia 124 Department of Physics, New York University, New York NY, United States of America 125 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo, Japan 126 Ohio State University, Columbus OH, United States of America 127 Faculty of Science, Okayama University, Okayama, Japan 128 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK, United States of America 129 Department of Physics, Oklahoma State University, Stillwater OK, United States of America 130 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 131 Center for High Energy Physics, University of Oregon, Eugene OR, United States of America 132 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France 133 Graduate School of Science, Osaka University, Osaka, Japan 134 Department of Physics, University of Oslo, Oslo, Norway 135 Department of Physics, Oxford University, Oxford, United Kingdom – 64 – 2019 JINST 14 P12006 136 LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris, France 137 Department of Physics, University of Pennsylvania, Philadelphia PA, United States of America 138 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 139 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA, United States of America 140 (a)Laboratório de Instrumentação e Física Experimental de Partículas — LIP, Lisbon; (b)Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisbon; (c)Departamento de Física, Universidade de Coimbra, Coimbra; (d)Centro de Física Nuclear da Universidade de Lisboa, Lisbon; (e)Departamento de Física, Universidade do Minho, Braga; (f)Universidad de Granada, Granada (Spain); (g)Departamento de Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica; (h)Av. Rovisco Pais, 1 1049-001 Lisbon, Portugal, Portugal 141 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 142 Czech Technical University in Prague, Prague, Czech Republic 143 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic 144 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom 145 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 146 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA, United States of America 147 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago; (b)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 148 Department of Physics, University of Washington, Seattle WA, United States of America 149 Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom 150 Department of Physics, Shinshu University, Nagano, Japan 151 Department Physik, Universität Siegen, Siegen, Germany 152 Department of Physics, Simon Fraser University, Burnaby BC, Canada 153 SLAC National Accelerator Laboratory, Stanford CA, United States of America 154 Physics Department, Royal Institute of Technology, Stockholm, Sweden 155 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY, United States of America 156 Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom 157 School of Physics, University of Sydney, Sydney, Australia 158 Institute of Physics, Academia Sinica, Taipei, Taiwan 159 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (b)High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 160 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 161 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 162 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 163 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 164 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 165 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 166 Tomsk State University, Tomsk, Russia 167 Department of Physics, University of Toronto, Toronto ON, Canada 168 (a)TRIUMF, Vancouver BC; (b)Department of Physics and Astronomy, York University, Toronto ON, Canada 169 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 170 Department of Physics and Astronomy, Tufts University, Medford MA, United States of America 171 Department of Physics and Astronomy, University of California Irvine, Irvine CA, United States of America 172 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 173 Department of Physics, University of Illinois, Urbana IL, United States of America 174 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia, Spain 175 Department of Physics, University of British Columbia, Vancouver BC, Canada 176 Department of Physics and Astronomy, University of Victoria, Victoria BC, Canada – 65 – 2019 JINST 14 P12006 177 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 178 Department of Physics, University of Warwick, Coventry, United Kingdom 179 Waseda University, Tokyo, Japan 180 Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 181 Department of Physics, University of Wisconsin, Madison WI, United States of America 182 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 183 Department of Physics, Yale University, New Haven CT, United States of America 184 Yerevan Physics Institute, Yerevan, Armenia aAlso at Borough of Manhattan Community College, City University of New York, New York NY, United States of America bAlso at CERN, Geneva, Switzerland cAlso at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France dAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland eAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain fAlso at Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal gAlso at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates hAlso at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece iAlso at Department of Physics and Astronomy, Michigan State University, East Lansing MI, United States of America jAlso at Department of Physics and Astronomy, University of Louisville, Louisville, KY, United States of America kAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva, Israel lAlso at Department of Physics, California State University, East Bay, United States of America mAlso at Department of Physics, California State University, Fresno, United States of America nAlso at Department of Physics, California State University, Sacramento, United States of America oAlso at Department of Physics, King’s College London, London, United Kingdom pAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia qAlso at Department of Physics, Stanford University, Stanford CA, United States of America rAlso at Department of Physics, University of Adelaide, Adelaide, Australia sAlso at Department of Physics, University of Fribourg, Fribourg, Switzerland tAlso at Department of Physics, University of Michigan, Ann Arbor MI, United States of America uAlso at Dipartimento di Matematica, Informatica e Fisica, Università di Udine, Udine, Italy vAlso at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia wAlso at Giresun University, Faculty of Engineering, Giresun, Turkey xAlso at Graduate School of Science, Osaka University, Osaka, Japan yAlso at Hellenic Open University, Patras, Greece zAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain aa Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany ab Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands ac Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia, Bulgaria ad Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary ae Also at Institute of Particle Physics (IPP), Vancouver, Canada af Also at Institute of Physics, Academia Sinica, Taipei, Taiwan ag Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan ah Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia ai Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid, Spain aj Also at Istanbul University, Department of Physics, Istanbul, Turkey ak Also at Joint Institute for Nuclear Research, Dubna, Russia al Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France – 66 – 2019 JINST 14 P12006 am Also at Louisiana Tech University, Ruston LA, United States of America an Also at LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris, France ao Also at Manhattan College, New York NY, United States of America ap Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia aq Also at National Research Nuclear University MEPhI, Moscow, Russia ar Also at Physics Department, An-Najah National University, Nablus, Palestine as Also at Physics Dept, University of South Africa, Pretoria, South Africa at Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany au Also at School of Physics, Sun Yat-sen University, Guangzhou, China av Also at The City College of New York, New York NY, United States of America aw Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China ax Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ay Also at TRIUMF, Vancouver BC, Canada az Also at Universita di Napoli Parthenope, Napoli, Italy ∗Deceased – 67 –