Recent studies in ATLAS and CMS experiments for the reconstruction and
identification of electrons and photons using full Monte Carlo and testbeam
data are reportedComment: Talk given at the Hadron Collider Physic Symposium 2006 (HCP2006),
  Durham, NC on behalf of ATLAS and CMS collaboration

Charlot, C.

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arXiv

Recent studies in ATLAS and CMS experiments for the reconstruction and identification of electrons and photons using full Monte Carlo simulation and test beam data are reported.Recent studies in ATLAS and CMS experiments for the reconstruction and identification of electrons and photons using full Monte Carlo and testbeam data are reporte

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Available on CMS information server CMS CR 2006/053CMS Conference Report8th September 2006Electron/Photon identification in ATLAS and CMSC. Charlot,Laboratoire Leprince-Ringuet, Ecole Polytechnique and IN2P3-CNRS, Palaiseau, Franceon behalf of ATLAS and CMS CollaborationsPresented at the Hadron Collider Physics Symposium 2006, Durham, USAbstractRecent studies in the ATLAS and CMS experiments on the reconstruction and identification of elec-trons and photons using full Monte Carlo simulation and test beam data are reported.1 IntroductionATLAS and CMS are the two multipurpose experiments under construction for the Large Hadron Collider(LHC) at CERN. As the LHC is expected to produce its first collisions in the end of 2007, both collaborationsare actively preparing the data taking and in particular developing their reconstruction tools. The detection ofelectrons and photons are of particular importance at the LHC as these particles intervene in the flagship H → γγand H →ZZ(∗) → 4e channels for the search of the Standard Model (SM) Higgs. Leptons are also importantin many SUSY scenarii as produced in the leptonic decays of charginos and neutralinos. Electrons and photonsalso appears in searches for TeV resonances that may come from new symmetries or as consequences of scenariosinvolving extra spatial dimensions. Last but not least, electrons appear in the final state of many standard modelprocesses involving top quarks or electroweak bosons, that constitute backgrounds to new signals or are intendedto be used as calibration processes.2 The ATLAS and CMS detectorsThe ATLAS detector is built around a set of large superconducting air-core toroid magnets providing a strongbending power for the measurement of muons. The toroids are complemented by a thin solenoid of 2T surroundingthe inner cavity in which the tracking system is located. The detection of electrons and photons relies in partic-ular on the LAr electromagnetic sampling calorimeter which is located after the solenoid and before the muonspectrometer and which provides angular coverage up to |η|=2.5 for precision measurement. Of particular impor-tance for the particle identification, the electromagnetic calorimeter is highly segmented with a 3-fold granularityin depth and an η × φ granularity of 0.003 × 0.1, 0.025 × 0.025, and 0.05 × 0.025 respectively in the front,middle and back compartment, totaling a thickness of 24X0 in the barrel part and of 26X0 in the endcap parts.A presampler with a fine granularity in η is located before the cryostat and the coil, enabling to correct for thecorresponding dead material effects. The inner tracker consists of a combination of discrete semi-conductor pixeland strip detectors in the innermost part and of continuous straw tubes in the outermost part providing further par-ticle identification capability through the production of transition radiation. More details on the ATLAS detectorand its performances can be found in [1]. Test beam measurements show a resolution of the LAr electromagneticcalorimeter of (10.0 ± 0.1)%/√E ⊕ (0.21 ± 0.03)% well matching the requirements. Recent test beam resultsfor high energy showers shown on Fig. 2(left) confirm this resolution with a precision of 0.83% for 180 GeV/cincident electrons. The uniformity of the response within a module is measured to be within 0.37% (rms).The CMS detector is based on a compact magnet delivering a 4T solenoidal field. The electromagneticcalorimeter, located inside the solenoid, consists of an homogeneous calorimeter made of 75848 PbWO4 scin-tillating crystals. The coverage for precision measurement extends up to |η| = 2.6. The crystal size correspondto an η × φ granularity of approximately 0.0174 × 0.0174 in the barrel part of the calorimeter which extendsup to |η| = 1.479. A preshower detector is located just before the endcap parts. The crystals are mounted in aquasi-projective geometry with a small angle (3◦) between the crystal axis and the line to the nominal vertex inboth η and φ directions. The crystal length corresponds to 25.8X0 in the barrel and 24.7X0 in the endcap partsof the calorimeter. The CMS inner tracker is made of 10 layers of silicon strip detectors complemented in theinnermost part by a set of pixel layers (3 additional layers in the barrel part and 2×2 endcaps disks in the forwardregions). The inner tracker provides a coverage of up to |η|=2.5. More details on the design and performances ofthe CMS detector can be found in [2]. Recent testbeam results show an excellent energy resolution of the PbWO4calorimeter as a function of the incident energy, as illustrated in Fig. 2(right). For central incidence, an energyresolution of 2.8%/√E ⊕ 0.125/E ⊕ 0.30% is measured [3].3 Electron and photon reconstructionThe reconstruction of electron and photon objects starts by the detection of clusters in the electromagneticcalorimeters. Both collaborations are considering the use of fixed windows for the definition of photon clustersand of fixed windows or topological clusters in the case of electrons. For photons, a 5×5 matrix is typicallyused (the number of cells in the ATLAS case refering to the middle compartment) while for electrons asymetricalwindows or topological clustering algorithms are used. Indeed, in the case of electrons, the energy emitted by thebremsstrahlung radiation in the tracker material has to be recollected to minimize the effect of lateral fluctuations.Detailed investigations of the calorimeters response in the test beam are used to characterize the constructedcalorimeter modules and study the many effects that need to be corrected for in order to reach the required pre-cision. A full deconvolution of the various effects is carried out by the ATLAS experiment using the test beamand Monte Carlo simulations. The clusterized energy of (intercalibrated) cells is corrected for longitudinal and lat-2Figure 1: Energy resolution of the ATLAS and CMS electromagnetic calorimeters as measured from test beams.(left) Energy distribution with the LAr calorimeter for a 180 GeV incident energy. (right) Energy resolution as afunction of the incident energy with the PbWO4 calorimeter (figure from [2]).eral leakage, dead material upstream of the calorimeter (making use in particular of the presampler measurementEV isPS ), bremsstrahlung losses, and the variations of the response due to impact point, according to:.Erec = [a(E)+b(E)×EV isPS +c(E)(EV isPS ×EV is1 )0.5+d(E)×∑Ecaloi ]×(1+fleak(depth))×fbrem(E)×fimpact(1)where the energy dependent correction factors are extracted from the simulation and checked with the test beam.The variation and losses of containment in CMS are corrected for by a single function of the number of clusterizedcrystals times an η dependent function parametrizing the energy lost in dead material and residual η dependenteffects. Figure 3(left) presents the containment variations for electrons in the CMS barrel, as obtained from sim-ulations. It is expected that the corrections will be finally tuned using Z → ee real data and that the Monte Carlowill be only used to extrapolate these corrections toward kinematical regions not accessible as toward lower pT(down to ∼10 GeV/c). This is illustrated on Fig. 3(right) which presents a comparison between the η dependentcorrection factors obtained with the simulation of single electrons and using Z → ee simulated data in CMS.Electrons are then characterized by the presence of a charge track pointing to the electromagnetic cluster, onthe contrary to photons which are characterized by the absence of a matching track. Starting from the reconstructedclusters, roads can be built to search for hits in the innermost part of the tracker. This procedure is used in CMSto define electron objects at the trigger level as well as for the offline reconstruction, where looser settings andthresholds are used to efficiently reconstruct electrons down to pT ∼ 10 GeV/c. As the bremsstrahlung radiationseverely affects the track propagation, a dedicated tuning of the trajectory building and a Gaussian Sum Filter trackfit are used in CMS. As a result, track hits can be collected up to the ECAL front face as shown on Fig. 3(left),while at the same time keeping a good momentum resolution at the vertex [4].A further property of the GSF track fit is to provide a meaningful estimate of the track momentum at thelast point. The relative difference between the momentum at the vertex and the momentum at the last point(bremsstrahlung fraction) can then be used as a measurement of the bremsstrahlung energy radiated by the electrontrack. Based on this measurement and on the number of observed sub-clusters, different track-cluster patterns(classes) are defined in CMS and used to derive specific corrections as well as estimates of the electron quality. Thefraction of electron population in the different classes is shown on Fig. 3(right) as a function of the pseudorapidity.The fraction of badly measured (showering) electrons follows the material budget of the inner tracker, while thefraction of well measured (golden) electrons is anti-correlated with the material budget distribution. Furthermore,as the bremsstrahlung fraction is related to the amount of material before the calorimeter, it can be used to controlthis amount from real data, in addition to usual variables sensitive to the integral amount of material as E/p. Arecent simulation study in CMS shows that a 2% precision could be obtained on X/X0 [4].The photon reconstruction is being studied in details using the test beam, for which incident electrons in theabsence of magnetic field and dead material are close to unconverted photons. Specific corrections, in particularfor the containments variations with respect to the incident position are derived from the test beam [6]. Photonreconstruction is also severely hampered by the presence of dead material before the calorimeter. The conversions3noiseσ(seed) > 2cry N0 5 10 15 20 25 30 35 40>e/Esc<E0.70.750.80.850.90.9511.051.1 < 20 GeVeE  < 40 GeVe E≤20 GeV   < 60 GeVe E≤40 GeV   < 80 GeVe E≤60 GeV  100 GeV≤  e E≤80 GeV Barrelη0 0.2 0.4 0.6 0.8 1 1.2 1.4Correction factor0.9811.021.041.061.08goldenshoweringFigure 2: Energy corrections. (left) Measured energy in CMS ECAL barrel as a function of the number of cluster-ized crystals above the noise (figure taken from [4]). (right) Comparison of the η dependent corrections functionsused in CMS and derived from single electron Monte Carlo simulation (lines), and the ones (symbols) obtainedusing Z → ee data (figure taken from [5]).Number of hits per track0 2 4 6 8 10 12 14 16 18 20a.u.00.050.10.150.20.25 e GSF tracksdefault KF tracksHLT electrons |eη| 0 0.5 1 1.5 2 2.5Fraction of electrons00.20.40.60.811.2Fraction of electronsgoldenbig bremnarrowshoweringFigure 3: GSF tracking in CMS. (left) Number of collected hits. (right) Fraction of electron population in thedifferent classes. The classification is based in particular on the bremsstrahlung fraction (see text). Figures takenfrom [4].4Eff. Sig./Eff. Bg.0 1 2 3 4 5 6 7 8Eff. Sig.0.750.80.850.90.951R < 0.30∆ (HCAL), tEΣR < 0.4∆ (HCAL), tEΣR < 0.3∆ (HCAL), t/EtEΣR < 0.4∆ (HCAL), t/EtEΣORCA H/Eγγ →H  + jetγBg: PYTHIA + Pile-upRejection0 5 10 15 20 25 30 35 40Signal efficiency0.80.850.90.951=0.15coneR=0.20coneR=0.25coneR=0.30coneR=0.35coneR=0.40coneRFigure 4: Isolation performances. Efficiency rejection curves for (left) rejection of the γ+jet background in H →γγ using HCAL based isolation and (right) rejection of the t¯t background in the H → 4e decay channel usingtrack based isolation. Figures taken respectively from [8] and [9].can be reconstructed using an inward track finding with an efficiency of ∼80% up to transverse radii of ∼65 cmin CMS. The reconstructed conversion tracks can then be fitted and used to determined the photon momentum aswell as the primary interaction vertex. A precision of ∼1.7 mm on the longitudinal vertex coordinate is obtainedfor conversions produced at a transverse radius rT within 15 < rT < 58 cm in the central part of the CMS detector[7].4 Electron and photon identificationFurther electron and photon identification is achieved using isolation, specific detectors such as the transitionradiation tracker (TRT) and the highly segmented first ECAL compartment in ATLAS or the preshower detector infront of the endcap part of the CMS electromagnetic calorimeter, and finally using shower shape variables and, inthe case of electrons, refined track-cluster matching.Isolation is a very powerful tool to reject jet backgrounds in both electron and photon identification. Figure 4shows the isolation performances obtained in CMS for the specific cases of the rejection of the γ+jet backgroundin the search for the Higgs boson in H → γγ [8], and for the rejection of the t¯t background in the H → 4e decaychannel [9]. Different working points can be chosen according to the needed background rejection. The resultsshown illustrate the performances obtained using HCAL isolation and track isolation. The combination of trackbased and calorimeter based isolation is also investigated [8].In ATLAS, the transition radiation emitted by charged particles in the outermost tracker can be further used toseparate electrons from isolated charged pions. Figure 4(left) presents the results obtained in test beam for 9 GeVincident electrons and compared to a Monte Carlo simulation. A good agreement is observed and a rejection factorof∼50 against pions is obtained for a 90% electron efficiency.A detailed study of the shower shape in the electromagnetic calorimeter has been performed in ATLAS exploit-ing the high longitudinal and transversal segmentation of the detector. An excellent agreement is found betweenthe Monte Carlo simulation and test beam data for both the mean longitudinal and transversal shapes. The transver-sal shower shape, in particular in the first compartment, can then be exploited to separate photons from pi0, onceisolation has been applied. The distributions of the fraction of energy outside the shower core in the first longitu-dinal segment of the ATLAS electromagnetic calorimeter as obtained from Monte Carlo simulation is presented inFig. 4(right). A rejection factor of ∼3.2 against pi0 is found for a 90% photon efficiency, with good a agreementbetween test beam data and simulated data.Refined track-cluster matching, both geometrical and in momentum, can be used to further separate electronsfrom jets after the application of isolation criteria. Apart from the momentum mismatch, jets or isolated pionsdistinguish from electrons by the shower shape which reflects in position mismatch and in other identificationvariables that can be built from track and cluster observables. Figure 4 (left) presents the E/p distributions obtainedfrom the simulation of single electrons and QCD di-jets events. As expected, jet induced showers don’t match in5Figure 5: e/pi and pi0/γ separation in ATLAS. (left) pion rejection as a function of the electron efficiency for 9GeV electrons using the transition radiation detector. (right) fraction of energy outside the shower core in the firstlongitudinal segment of the LAr calorimeter for γ and pi0. (rad)outφ∆0 0.005 0.01 0.015 0.02 0.025 0.03a.u.-310-210-110goldennarrowbig bremshoweringjets (all classes)Figure 6: Distributions of identification variables. (left) E/p distribution for electrons and jets from QCD di0jetevents as expected in ATLAS. (right) Difference in φ between the cluster position and the track extrapolation fromthe outermost track hit for jets and for the different electron classes defined in CMS (figure taken from [9]).energy with the track momentum.In CMS, electron classes based on the different observed track-cluster patterns are used to optimize the electronidentification using probability distributions per class. As an illustration, the difference in φ between the clusterposition and the track extrapolation from the outermost track hit is presented in Fig. 4 (right). The distributionsobserved for golden electrons and jets are clearly different. Not surprisingly, showering electrons which containselectrons with identified bremsstrahlung clusters are found more difficult to separate from jets. Further details canbe found in [4]. The electron identification is finally optimized by combining the different identification variablesin a neural network or in a likelihood function.Finally, both ATLAS and CMS are considering the use of a dedicated algorithm for the identification and thereconstruction of electrons in jets. The ATLAS low pT electron algorithm starts from the track reconstructionand then looks for an energy deposition in the electromagnetic calorimeter around the extrapolated track position.The cluster properties are calculated and compared to probability distribution functions or injected into a neuralnetwork. A similar procedure is being studied in CMS for the reconstruction of electrons in jets. Figure 4 showsthe pion rejection obtained in ATLAS as a function of the electron identification efficiency for J/Ψ, WH and t¯t H6Figure 7: Performances on single tracks of the ATLAS low pT electron algorithm. Pion rejection is shown as afunction of the electron identification efficiency in J/Ψ events, WH events and t¯tH events with H → bb¯.events where H → bb¯. A 60% soft b-tagging efficiency is obtained for a rejection factor of 150 on WH events,which can be used to complement secondary vertex finding algorithms.5 AcknowledgmentsThe author would like to thank D. Zerwas from the ATLAS Electron/Photon working group as well as C. Seezand Y. Sirois from the CMS ECAL/Egamma working group for the interesting discussions and comments duringthe preparation of the talk and of this report.References[1] ATLAS Collaboration, ATLAS Detector and Physics Performance Technical Design report, CERN/LHCC99-15 (1999).[2] CMS Collaboration, CMS Detector Performance and Software Physics Technical Design Report, Vol. I,CERN/LHCC 2006-001 (2006).[3] P. Adzic et al., Energy resolution performance of the CMS electromagnetic calorimeter, CMS Note in prepa-ration.[4] S. Baffioni et al., Electron reconstruction in CMS, CMS Note 2006/040.[5] R. Paramatti et al., Use of Z → e+e− events for ECAL calibration, CMS Note 2006/039.[6] J. Descamps et al., Periodic position dependence of the energy measured in the CMS electromagnetic calorime-ter, CMS Note 2006/045.[7] N. Marinelli, Track finding and identification of converted photons, CMS Note 2006/005.[8] M. Pieri at al., Distinguishing Isolated Photons from Jets, CMS Note 2006/007.[9] S. Baffioni at al., Discovery potential for the SM Higgs boson in the H → ZZ(∗) → e+e−e+e− channel,CMS Note 2006/115.7

Electron/Photon identification in ATLAS and CMS

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