Lepton and Photon reconstruction and identification performance in CMS and ATLAS

Lepton and Photon reconstruction and identification performance in CMS and ATLAS Sandro Fonseca de Souza, on behalf of the CMS and ATLAS Collaborations Universidade do Estado do Rio de Janeiro Large Hadron Collider Physics Conference- LHCP 04-09 June 2018 - Bologna - Italy

Outline Presentation of the performance results of both collaborations using Run II data for CMS and ATLAS Experiments Muons: Trigger Studies Isolation Scale and Resolution Electrons Trigger Studies Isolation Energy Calibration Photons Identification Isolation Energy Calibration Examples of Higgs results in leptons and photons decays Summary This talk is covering 2015, 2016, 2017 (CMS/ATLAS) and 2018 (ATLAS Muon Scale Resolution only) data taken presented in public results 2

Detector Description CMS Experiment ATLAS Experiment 3

Muons Reconstruction and Identification [toroid magnets] Muon Spectrometer-MS Calorimeters Inner detector-id [solenoid magnetics] 1. Local hit - segment reconstruction (RPC - DT/CSC) 2. Reconstruction of muon spectrometer stand-alone track(s) (pt estimation) 3. Reconstruction of inner track(s) using silicon detector 4. Global Muon are defined from standalone + inner tracks (combined fit performed - pt re-evaluated: outside-in) 5. Inside-out identification of tracker muons (by matching inner tracks with CSC/DT segments) 6. Plus more, e.g. : Ad hoc high-pt refits Computation of isolation quantities around muons (both based on detector quantities and particle flow ones) 4 Combined muons (combine Inner Detector (ID) and Muon Spectrometer (MS) measurements) Standard method used in ATLAS Standalone muons: MS only (at high η, near to the beam axis) Calo-Tagged muons: ID tracks with additional small energy deposits in the calorimeter (at η 0) Segment-Tagged muons: ID tracks combined with single segments of the MS (at low energies)

Muons Reconstruction and Identification [toroid magnets] Muon Spectrometer-MS Calorimeters Inner detector-id [solenoid magnetics] 1. Local hit - segment reconstruction (RPC - DT/CSC) 2. Reconstruction of muon spectrometer stand-alone track(s) (pt estimation) 3. Reconstruction of inner track(s) using silicon detector 4. Global Muon are defined from standalone + inner tracks (combined fit performed - pt re-evaluated: outside-in) 5. Inside-out identification of tracker muons (by matching inner tracks with CSC/DT segments) 6. Plus more, e.g. : Ad hoc high-pt refits Computation of isolation quantities around muons (both based on detector quantities and particle flow ones) 5 Combined muons (combine Inner Detector (ID) and Muon Spectrometer (MS) measurements) Standard method used in ATLAS Standalone muons: MS only (at high η, near to the beam axis) Calo-Tagged muons: ID tracks with additional small energy deposits in the calorimeter (at η 0) Segment-Tagged muons: ID tracks combined with single segments of the MS (at low energies)

Reference:CMS-MUO-16-001- Submitted to J. Instrum. Reference: ATL-PHYS-PROC-2017-123 Muons: Trigger Efficiencies TighID TighID Absolute efficiency of Level 1 (L1) MU20, and absolute and relative efficiencies of the OR of mu26_ivarmedium with mu50 High Level Triggers (HLT). Medium muons are used. Triggers include isolation and pt μ cuts. TighID Single (isolated) muon trigger efficiency with Tag and Probe (Z μμ events) Tight ID and PF isolation requirements might use the isolated single-muon trigger to select events Overall high efficiency for single muon triggers: <ε> ~95% Very good data/mc agreement The efficiency for isolated single-muon triggers is improved with respect to Run 1 is about 10% for η > 1.2 but reaches 20% for η 2.4 6

Reference:CMS-MUO-16-001- Submitted to J. Instrum. Reference: Muon Combined Performance (MCP) presented at DIS 2018 Muons: Isolation Studies Good agreement between data and MC simulation is always ~0.5 % Z μμ (two global muons) pt>20gev Z μμ (two global muons) pt>20gev The tight muon to also satisfy tight isolation requirements is about 5% in the barrel and goes up to about 15% in endcap Measure detector activity around a muon candidate for background rejection Use track-based & calorimeter-based variables Overall good agreement between data and simulation 2015 data = about 2/fb of p+p collisions collected in 2015 7

Reference:CMS-MUO-16-001- Submitted to J. Instrum. Reference: MUON-2018-001 Muons: Scale and Resolution Peak of the dimuon invariant mass (a) and dimuon mass resolution (b) as a function of pseudorapidity (η) obtained from reconstructed Z μ+μ candidates 2018 Data (a) Momentum Resolution Method used by CMS experiment Use well-reconstructed cosmic rays, passing through the pixels, reconstructed in upper and lower halves of a CMS detector Compute relative residual Compared with 2010, resolution improvement of about 25% at high pt Tune-P algorithm has undergone significant modifications Better alignment Two opposite-charge muons with 22 GeV < pt < 300 GeV The invariant mass of the dimuon system must be in the range of 75 GeV <mμ+μ <105 GeV 2018 Data (b) 8 A fit to data and simulation of a Crystal-Ball (CB)+ Breit-Wigner Systematic uncertainties from ±1σ variations

Electrons Reconstruction and Identification ECAL Driven Algorithm Finds ECAL superclusters with ET > 4 GeV A supercluster is a group of one or more clusters of energy deposits in the ECAL Takes into account the spread of energy in φ due to electrons radiating in the tracker from the B-field Matches superclusters to track seeds (pairs or triplets of hits) Electron tracks produced from these seeds Topological Superclusters Algo Dynamic superclusters built from topo- Introduced for offline identification in 2017 Alternative to sliding-window algorithm clusters showers, O(100 MeV) photons improved energy response linearity Allow reconstruction of low energy recover the low-energy bremsstrahlung less leakage energy, less corrections needed In 2017, silicon detector suffered an update to add 4th layer and reduce material budget in the endcaps. (Coverage up to η <2.5) Cluster reconstruction in ECAL Common for both electrons and photons (Electrons also reconstructed as photons) Designed to collect bremsstrahlung and conversions in extended phi region Dedicated track reconstruction for electrons Gaussian Sum Filter (GSF) allows for tracks w/ large bremsstrahlung 9 ATLAS Reconstruction algorithmic built clusters from seeds using size Δη Δφ 0.075x0.175 (0.125x0.125) in barrel (endcap). The rectangular cluster is used in analyses before to 2017. Track (extrapolated into calorimeter) matched within Δη<0.05, ΔΦ<0.05 ( -0.1 < dphi < 0.05) If several tracks: priority of tracks with Si hits closest match in ΔR T a k e e n e r g y f r o m c l u s t e r, e l e c t r o n direction from track parameters GSF fit that is generalisation of the Kalman fitter, splitting experimental noise into Gaussian components, using it to process each one Identification parameters Calorimeter shapes Shower shapes in layer 1 and 2 Leakage into the hadronic calorimeter Tracking information Track quality and impact parameter cuts Track cluster matching (Δη, ΔΦ) Use of TRT (see description in slide 30) info Ratio of high threshold hits/trt hits Common set of calorimeter discriminating variable: Cut-based selection for photon ID and Likelihood-based for electrons

Reference: CMS DP -2017-004 Reference: ATL-COM-DAQ-2017-015 Electrons: Trigger Efficiencies The efficiency is measured by a Z in ee tag-and-probe method with respect to cut-based tight electron identification, using several HLT electron trigger paths The efficiency is measured by a Z in ee tag-and-probe method with respect to cut-based loose electron identification, using a single HLT electron trigger Trigger Efficiency 1.4 1.2 1 0.8 ATLAS Preliminary Data 2016, -1 s = 13 TeV, 33.5 fb 0.6 0.4 0.2 HLT_e26_lhtight_nod0_ivarloose OR HLT_e60_lhmedium_nod0 OR HLT_e140_lhloose_nod0 Data Z ee MC (a) 0 20 40 60 80 100 120 140 Offline electron E T [GeV] In (a) Eff vs ET presents threshold unprescaled single electron trigger combination and In (b) Eff vs η presents threshold unprescaled double electron trigger Single Triggers Trigger Efficiency 1.4 1.2 1 0.8 ATLAS Preliminary Data 2016, -1 s = 13 TeV, 33.5 fb (b) 0.6 0.4 HLT_e17_lhvloose_nod0, offline electron E > 18 GeV T Data Double Triggers 10 0.2 0 Z ee MC 2 1.5 1 0.5 0 0.5 1 1.5 2 Offline electron η Good agreement between MC and Data

Reference: CMS DP -2018-017 Reference: ATLAS-CONF-2016-024 Electrons: Isolation Performance Electrons isolation data/mc agreement was significantly improved with updated calibrations. The sum of transverse energies of charged hadron candidates in a ΔR=0.3 cone around the electron, divided by the electron transverse momentum in EC AL Barrel (isolation cut removed) Efficiencies of the FixedCutLoose electron isolation WP. FixedCutLoose are defined as: E T < 0.20 ET, where the sum if over all clusters in a cone of size R = 0.2 p T < 0.15 ET, where the sum is over all tracks (with pt > 1 GeV) in a cone of size R = min(0.2,10/et) Good agreement data/mc The sum of transverse energies of neutral electromagnetic candidates in a ΔR=0.3 cone around the electron, divided by t h e e l e c t r o n t r a n s v e r s e momentum in ECAL Barrel (isolation cut removed) The sum of transverse energies of neutral hadron candidates candidates in a ΔR=0.3 cone around the electron, divided by t h e e l e c t ro n t r a n s ve r s e momentum in ECAL Barrel (isolation cut removed) 11

Photon Reconstruction and Identification Preshower Barrel supermodule ECAL CMS ECAL is very fast and radiation-tolerant to survive in the LHC environment and it possesses excellent energy resolution Homogeneous PbWO4 crystal calorimeter Photon reconstruction in 5x5 crystal clusters and 50 GeV photons (~97% of unconverted photon energy) >50% probability to convert into e+e-pair spreads in φ due to B-field super-clusters (SC) Barrel region narrow in η: 5 crystals Barrel long in φ: ±10-15 crystals Endcap region from 5x5 crystal around most energetic + preshower Endcap 12 Topological superclusters algorithm is introduction for photonid off-line in 2017 ATLAS Photon Reconstruction algorithmic built clusters from seeds using size 0.075x0.175 for unconverted γ s and size 0.075x0.175 for conversions in barrel; use 0.125x0.125 in endcap used in analyses before to 2017. Check if the conversion or tracks are pointing to cluster No track: unconverted photon Conversion: converted photons Apply cluster calibration Measure direction Use φ from measurement in layer 2 Use pointing (η measurement in 1st and 2nd EM layer) Converted γ: take photon direction from conversion and pointing Identification parameters Calorimeter shapes Shower shapes in layer 1 and 2 L e a k a g e i n t o t h e h a d r o n i c calorimeter Cuts different for converted and unconverted γ s however they are optimised in various η bins (but pt independent)

Reference: CMS DP -2017-004 Reference: ATL-EGAM-2017-010 Photons: Identification Efficiencies Efficiency measured in data with the tag and probe method using Z ee reconstructed as photons shown in 4 pseudorapidity ranges as a function of the photon transverse energy Efficiency of photon tight identification requirement for unconverted photons from radiative Z decays into an e+e- or mu +mu- pairs as a function of the average number of pp interactions per bunch crossing (<μ>), for the entire pseudorapidity region. A loose photon isolation requirement is applied. LooseID High pt, Eff ~ 90% Eff ~ 88% 2016 Data pt> 20, Eff ~ 80% LooseID 2017 Data High pt, Eff ~ 90% The uncertainty on the efficiency measurement is typically smaller than 1% for ET ~ 60 GeV (relevant for H > yy) pt> 20, Eff ~ 60-80% Presentation at CALOR 2018-21 May 2018 13 This plot corresponds to the old photon reconstruction using fixed size clusters; the results are comparable for the new reconstruction using dynamical clusters

Reference: CMS DP -2018-017 Reference: ATL-EGAM-2017-010 Photons: Isolation Performance Photon isolation data/mc agreement was significantly improved with updated calibrations. Sum of transverse energy of the charged hadrons around the selected photons Efficiency of isolation requirement FixedCutLoose, defined as ET,iso(calo)/ET<0.065 and ET,iso(track)/ET<0.05 as a function of the average number of pp interactions per bunch crossing (<μ>) Eff ~ 78% Sum of transverse energy of the photons around the selected photons Eff ~ 84% Sum of transverse energy of the neutral hadrons around the selected photons MC: simulation was DY using Madgraph generator 14

Presentation at CALOR 2018-21 May 2018 References: EGAM-2018-001 and ATL-PHYS-PUB-2016-015 Electrons/Photons: Energy Calibration ECAL-Barrel The final part of the calib, the insitu correction using Z -> ee data E data = E MC (1 + ) (E) E = p a b E E c data MC (E) (E) = c 0 E E The Z-mass peak is visibly improved by updating the calibration The energy scales are derived for electrons with mean transverse momentum of 40 GeV, then extrapolated to other energies. Resulting in 0.2-0.3% uncertainty for photons with ET around 50-60 GeV. 15

Reference: CMS DP -2017-004 Reference: ATL-PHYS-PUB-2016-015 Electrons/Photons: Energy Calibration Fit Z Mass vs Z pt distribution. Events / 0.5 GeV 10 1.2 ATLAS Preliminary -1 1 s=13 TeV, 36.1 fb Z ee 0.8 6 Calibrated data Corrected MC Scale factor uncert. 0.6 0.4 0.2 Data / MC 0 1.1 1.05 1 0.95 0.9 80 82 84 86 88 90 92 94 96 98 100 [GeV] m ee Z ee mass distribution reconstructed with electron tuned energy corrections (sub-)leading electron pt>25(20) GeV and medium id in both plots 16 m ee distribution from Z ee in data compared to simulation after the application of the full calibration Good agreement between MC and Data Energy calibration is the checks with J/Psi and photon from radiative Z decays Very good stability with increasing pileup

Higgs boson results@ 13 TeV Higgs -> ZZ-> 4 l 17 Higgs -> 2 photons

Summary We can observe nice reconstruction performance measurements using lepton and photon objects in both experiments during Run-II data taken, even with the increase of instantaneous luminosity and Pile-up events Both CMS and ATLAS Experiments are using different technologies to detect these physics objects. However, they have presented good results concerning identification, resolution and triggered to make extensive coverage in the transverse momentum, pseudorapidity and invariant mass regions. Therefore, this enables to explore all physics program proposed for both experiments for Run-II and beyond. 18 Thank you for your attention

Backup slides 19

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Muon system overview (DTs) 21

Muon system overview (CSCs) 22

Muon system overview (RPCs) 23

Muon Reconstruction Overview on CMS 24

Muon Reconstruction ID WP Loose muon identification (ID) aims to identify prompt muons (originating at the first primary vertex) and muons from light and heavy flavor decay, as well as maintain a low rate of the misidentification of charged hadrons as muons Medium muon ID is optimized for prompt muons and for muons from heavy flavor decay. A medium muon is a loose muon with a tracker track that uses hits from more than 80% of the the inner tracker layers it traverses. Tight muon ID aims to suppress muons from decay in flight and from hadronic punch-through. A tight muon is a loose muon with a tracker track that uses hits from at least six layers of the inner tracker including at least one pixel hit. The muon must be reconstructed as both a tracker muon and a global muon Soft muon ID is optimized for low-p T muons for B-physics and quarkonia analyses. A soft muon is a tracker muon with a tracker track that satisfies a high purity flag and uses hits from at least six layers of the inner tracker including at least one pixel hit High momentum muon ID is optimized for muons with p T > 200 GeV. A high momentum muon is reconstructed as both a tracker muon and a global muon 25

Reference:CMS-MUO-16-001- Submitted to J. Instrum. Reference: MUON-2018-001 Muons: Identification Efficiencies Reconstruction efficiency > ~98% for medium muons in η > 0.1 as a function of the pt of the muon in the region 0.1< η <2.5 Z μμ (two global muons) pt>20gev The loose ID efficiency exceeds 99% over the entire range, and the agreement between data and simulation is better than 1%. 2018 Data The tight ID efficiency varies between 95% and 99% and the agreement with simulation ranges from 1% to 3%. The dips in efficiency close to eta = 0.3 are due to the cracks between the central muon wheel and the two neighboring wheels pt>20gev Z μμ (two global muons) Muon reconstruction efficiencies for the Medium identification algorithms measured in Z μμ events as a function of the muon pseudorapidity for muons with pt>10gev 2018 Data 2015 data = about 2/fb of p+p collisions collected in 2015 26

CMS Muons: Identification Efficiencies TnP definitions Probe = single tracker tracks associated with tag Single tracks reduce combinatorial bkgd at low pt Standard technique: tag + probe invariant mass fit with signal and background Signal = sum of 2 Voigtians (Gaussian Lorentzian) Background = exponential Efficiency = ratio of signal normalization factors, numerator has probes that pass selection Plots contain statistical errors only Systematic errors ~1%, dominated by backgrounds Estimated by varying pt and isolation cuts on tag muon 27

Particle Flow on CMS Particle Flow (PF) reconstruction: global event reconstruction paradigm outputs a list of particles identified across different detectors identify the primary vertex (PV) from PU uses particles from PV to build jets, compute missing-et, lepton isolation... 28 arxiv:1706.04965

CMS ECAL- Homogeneous Electromagnetic Calorimeter 29

CMS ECAL - Lead tungstate crystals 30

CMS ECAL - Overview 31

CMS ECAL - Energy Reconstruction 32

CMS - Electromagnetic Showers 33

CMS - Photon Characteristics 34

CMS - Photon Reconstruction 35

CMS - Electron Characteristics 36

CMS - Electron Reconstruction 37

CMS - Shower Shape Variables 38

CMS - E/Gamma Isolation 39

PF Photon Isolation for 2016 Legacy re-reco Reference: CMS DP -2018-017 Reconstructed with updated detector calibrations (legacy re-reco). 40

Presentation at CALOR 2018 with 2016 and 2017 datasets Reference: ATLAS-EGAM-2018-002 Electrons: Identification Efficiencies Efficiency measured in data with the tag and probe method using Z ee is shown in 4 pseudorapidity ranges Electron identification efficiencies in Z ee and J/ψ ee events as a function of transverse energy ET (a) and eta (b), integrated over the full pseudo-rapidity range for the efficiency in the central region Electron LooseID cutbased Identification ε > 90% for pt e > ~30 GeV ε > 85-95% for pt e > ~80 GeV 2017 Data (a) 2017 Data Electron TightID cutbased Identification ε > 70% for pt e > ~30 GeV ε ~ 90% for low eta region 41 (b)

Reference: CMS DP -2017-004 Reference: ATL-EGAM-2017-010 Photons: Identification Efficiencies Efficiency measured in data with the tag and probe method using Z ee reconstructed as photons shown in 4 pseudorapidity ranges as a function of the photon transverse energy LooseID High pt, Eff ~ 90% Efficiency of photon tight identification requirement for unconverted photons (a) and converted photons (b) from radiative Z decays into an e+e- or mu+mu- pairs as a function of the average number of pp interactions per bunch crossing (<μ>), for the entire pseudorapidity region. A loose photon isolation requirement is applied. Eff ~ 88% pt> 20, Eff ~ 80% (a) LooseID High pt, Eff ~ 90% (b) pt> 20, Eff ~ 60-80% Eff ~ 90% Presentation at CALOR 2018-21 May 2018 42

ATLAS - Inner Tracker Semiconductor Tracker Transition Radiation Tracker 43

ATLAS - Transition Radiation Tracker 44

ATLAS - Lead-LAr sampling EM calorimeter 45

ATLAS - Lead-LAr sampling EM calorimeter 46

The Electron and Photon Trigger-Overview 47

Electron and photon energy calibration with the ATLAS detector - brief overview MVA calibration In-situ scale factors 48

ATLAS-e/γ-Topological Superclusters 49

ATLAS-e/γ-Discriminating Variables 50

Muon System Overview on ATLAS 51

Muon System Overview on ATLAS 52

Muon Reconstruction Overview on ATLAS 53

Muon Reconstruction ATLAS WP definition 54

ATLAS Muon Scale/Resolution 55