CMS electron and photon performance at s = 13 TeV · • Updated results for 2016 data (36.3 fb-1): • New data processing, with improved low-level calibration (legacy re-reco) •

CMS electron and photon performance at √s = 13 TeVFrancesco Micheli on behalf of CMS Collaboration

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Francesco Micheli ETH Zuerich

Electrons and Photons @ CMS• Electrons and photons are crucial for CMS physics program:

• SM precision physics, Higgs coupling measurements, BSM searches…

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• Selection of CMS results in this talk:

• Updated results for 2016 data (36.3 fb-1):

• New data processing, with improved low-level calibration (legacy re-reco)

• New results for 2017 data (42.6 fb-1):

• Interesting results @ICHEP using tools shown here

2017

2016

• Talk covers most important updated results, stressing improvements due to new detector conditions for the different years


Improvements for e/𝜸 reconstruction

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• Photon (Electron) in CMS:

• Cluster of energy deposits in ECAL crystals (matched to a track in the silicon tracker), see here for details about detector [CMS overview @ ICHEP]

• Big effort to maintain good efficiency in evolving data taking/detector conditions

https://indico.cern.ch/event/686555/contributions/2973801/



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www.company.com

The CMS ECAL

Luca Brianza

Barrel (EB): |η|<1.479, 36 supermodules 61200 crystals, 2.2x2.2x23 cm (25.8 X0)

Avalanche Photo-Diodes (APD)

Endcap (EE): 1.479 < |η| < 3.0, 2 sides 7324 crystals each side, 2.9x2.9x22 cm (24.7 X0)

Vacuum Photo-Triodes (VPT)

Preshower (ES): 1.65 < |η| < 2.6 Lead/silicon strips, 1.9x61 mm x-y view (3 X0)

Lead tungstate (PbWO4) homogeneous crystal calorimeter

Tracker coverage |η|<2.5 ECAL placed inside the magnetic coil

205/10/16

75848 Lead tungstate crystals (PbWO4) Cylindrical structure: Barrel (EB) + Endcaps (EE) + Preshower (Lead/Silicon Strips)

Electromagnetic calorimeter (ECAL)2016

• Legacy reReco of 2016 data is expected to improve significantly ECAL reconstruction

• Data reprocessed with improved low-level calibration

• Improved ECAL pedestals and calibrations


Francesco Micheli ETH Zuerich 5




Silicon Tracker

• New pixel detector in 2017 with added 4th layer and reduced material budget in the endcaps:

• Pixel 4th layer/quadruplet seeding reduces number of fake tracks

• More external layer closer to strip detector → better extrapolation of tracks

2017


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Reconstruction

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A Simplified Cartoon of the Electron Path

• Picture is similar for photons.

• Also seeding from Tracker hits matching clusters

(complementing at low pT

, called ”tracker driven”).

• Refining: adding clusters matching extrapolated tangents

at tracker layers, recovering bremsstrahlung or conversions.

particle-flow

Hybrid search: track and ecal drivIntroduction Reconstruction Identification Conclusions gg 5/ 20

• Reconstruction algorithms exploiting interplay between clustering and tracking to achieve best resolution

• Identification selection based on high level variables to remove fakes

• High Level Trigger (HLT) and reconstruction following similar path:

• Requiring isolation, Et cuts and compatibility ECAL/tracker for electrons + additional corrections (gaps, energy loss…)

• Seeding from ECAL hits (Tracker hits seeding important for low pt electrons)

• Brem and photon conversion recovery are of paramount importance


2016 - Legacy reReco

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2016 2016

• ECAL improved reconstruction for 2016 has visible good effects

• Main effect is better data/MC agreement → better performance, lower SF

• Example of pseudorapidity width of the supercluster for photons (EE)


2016 - Legacy reReco

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2016 2016

• ECAL improved reconstruction for 2016 has visible good effects

• Main effect is better data/MC agreement → better performance, lower SF

• Example of pseudorapidity width of the supercluster for photons (EE)

Electron Gsf tracking efficiency in 2016 legacy

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• Electron reconstruction efficiency indata (top) and data to simulatedefficiency ratios (bottom).

• The efficiency is measured with the tagand probe method and shown as afunction of supercluster η.

• The plot on the right shows for electronswith 20 < pT < 45 GeV. The efficiencyand the scale factors are similar for theother pT range.

• The error bars represent the combinedstatistical and systematic errors.

• There is an improvement in 2016 legacyre-reco compared to 2016 re-reco andexists across all pT range.

2016

Significant improvement of

reconstruction efficiency on

the whole eta/pt range for 2016

This improvements will be used

for final Run II CMS results


2017 - High level trigger (HLT)

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Efficiency of HLT_Ele32_WPTight pathas a function of SC ET

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• HLT measured with TnP method on Z→ee, visible effects due to new pixel:

• Reduced rate (despite higher pileup), especially at low pT:

• Rate reduction up to 70% for low pT double electron triggers

• Improved efficiency in the endcap for single electron pathsEfficiency of HLT_Ele32_WPTight pathas a function of SC ET

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Single isolated Electron pT>32, tight ID and isolation requirements


2017 - Reconstruction

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5 < ET < 20 GeV

ET > 20 GeV

• The number of unmatched reconstructed electron candidates (i.e. before the ID step) with pT > 20 GeV (top) and with 5 < pT < 20 GeV (bottom) and |η| < 2.5 in 2016 and 2017 DY+Jets MC versus the true number of pileup.

• The suppression in 2017 is attributed to the new pixel detector.

• Note that the fake rate gets further reduced by the identification step which is not applied here.

Fraction of Fake Electrons2016 vs 2017

• Electron reconstruction efficiency in data (top) and data to simulated efficiency ratios (bottom).

• The efficiency is measured with the tag and probe method and shown as a function of supercluster η.

• The error bars on the data/MC ratio represent the combined statistical and systematic errors.

Electron Gsf tracking efficiency

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2017

RECO

20172016

• 2017 reconstruction efficiency ~96% over the full Et/η spectrum:

• Similar efficiency wrt 2016, Increase of efficiency in some regions (low pt, high eta)

• In 2017, the fake rate is lowered due to the new pixel detector, by 30%: • Additional reduction of fake rate after the Identification step

• Fake rate and reconstruction efficiency measured on on Z→ee (TnP method)


Identification• Ele/𝜸 identification variables to separate from backgrounds (jets,

conversion, non-prompt particles):

• Calorimetric shower shape observables

• Isolation

• Tracking observables (+matching with clusters)

• Several selections are derived depending on the analysis, based on this variables (MVA and cut-based approach):

• Several working points (WP)

• Different selections EB/EE

• Data/MC scale factors derived for each selection

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2017- Identification

• MVA and cut-based approach with several working points (separate optimization depending on eta/pt)

• Improved performance for photons and electrons, in both barrel and endcap

• Upgraded pixel: electron fake rate in endcaps reduced by 20 % (at 90% efficiency)

• Updates of Electron MVA ID in 2017: • Uses more advanced machine learning techniques • Optionally includes PF isolation components (more flexible)

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2017

Electrons Photons

2017

2018 data taking is ongoing.

CMS is performing well, we are already updating

our E/gamma strategy for 2018.

Updated results soon, stay tuned!

• Dielectron invariant mass

• 2018 ‘Prompt’ reconstruction:

• Single electron trigger

• Minimal selection on electron pT and identification, EB and EE together

• Opposite charge for the two electrons

2018 - First look at data2018

2016 2017


Conclusions• First data processing of 2017 for e/gamma shows excellent performance:

• Improved HLT/Reconstruction/Identification efficiency, despite harsh experimental conditions

• Final 2016 reprocessing significantly improves data/MC agreeement:

• Analyses can exploit the understanding of low-level ECAL calibrations for final results

• Reconstruction algorithm and sophisticated MVA techniques always maintained and improved:

• Good modelling of physics effects and good separation from background ensures good physics performance for CMS physics program

• 2018 Data Taking ongoing:

• First look at data shows excellent quality, updated results soon

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BACKUP

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Identification - input variables

• fbrem = (pin-pout) /pin, fraction of the momentum lost to bremsstrahlung in tracker:

• Good way to check accuracy of MC simulation

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2017 2017


The fbrem Variable

• fbrem = (pin � pout) /pin, fraction of the momentum lost to

bremsstrahlung in tracker.

• Excellent tool to access the material budget and compare it

over data and MC.

• Discrepancies in Run I data/MC ) mismodelling of the

material budget ) corrected for in Run II.

Run I Endcaps

Introduction Reconstruction Identification Conclusions gg 11/ 20

1.5|⌘| < 2.0

2.0|⌘| < 2.5

fbrem Barrel, 2017 Endcap, 2017

Identification

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• Improved agreement wrt Run I → correction of mismodeling of material budget

• fbrem = (pin-pout) /pin, fraction of the momentum lost to bremsstrahlung in tracker:

• Good way to check accuracy of MC simulation

2017


2016 - ID improvement

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Electron Selection E�ciencies in Reprocessed 2016 Data

Electron cut based identification before and after 2016 data reprocessing:

• Data/MC agreement substantially improved due to final calibrations applied.

Introduction Reconstruction Identification Conclusions gg 18/ 20

Tight WP before Tight WP after

• Electron cut based identification reReco vs Legacy


Reconstruction algorithm

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Documents

CMS electron and photon performance at s = 13 TeV · • Updated results for 2016 data (36.3 fb-1): • New data processing, with improved low-level calibration (legacy re-reco) •