Introduction

  • PLImprovedTight and PLImprovedVeryTight are two new working points based on the PromptLeptonImprovedVeto algorithm.
  • PromptLeptonImprovedVeto is the improved version of the prompt lepton tagging algorithm developed in 2019-2020 by Fudong He and Rustem Ospanov.
    • Hanlin Xu has helped with developing the electron version.
    • The previous versions of these algorithms are called PromptLeptonVeto and PromptLeptonIso and described in PromptLeptonTagging.

Presentations

PromptLeptonImprovedVeto

  • Several new techniques were developed in order to identify and reject the non-prompt electrons and muons candidates from decays of heavy flavour (b and c) hadrons.
  • We designed novel isolation variables that are sensitive to the properties of heavy flavour jets.
  • We developed a dedicated secondary vertex reconstruction algorithm to identify the non-prompt leptons produced in the b- and c-hadron decays. Reconstructed secondary vertices are used to estimate flight path of non-prompt leptons.
  • We designed and optimised a recurrent neural network (RNN) using inner detector track impact parameters.
    • Electron RNN was trained to separate prompt electrons from non-prompt electrons and from fake electrons produced in material photon conversions.
    • Muon RNN was trained to separate prompt muons from non-prompt muons.
    • Top quark pair Monte-Carlo simulation samples were used for prompt and non-prompt lepton samples. Z boson with photon production was used for electron fakes from photon conversions.
  • Boosted Decision Tree algorithm was carefully designed and optimised to combine the isolation parameters, lifetime information, and the RNN output.
    • Three separate BDTs were trained: one for muons, one for barrel region (|η|<1.37) and another endcap region (|η|>=1.37) electrons.
    • The choice of the barrel or endcap BDT version for electrons is done internally by the isolation tool.
    • Top quark pair Monte-Carlo simulation samples were used for prompt and non-prompt lepton samples.
    • BDTs were trained to separate prompt leptons from non-prompt leptons.
  • The input variables are summarised in the table below:
    pliv_inputs.png

Working points

  • PLImprovedTight and PLImprovedVeryTight selection criteria were optimised as a function of the lepton pT in order to provide better non-prompt lepton rejection than the tightest previously available isolation working point, while providing similar prompt lepton efficiency.
  • PLImprovedTight was optimised to provide of about a factor of two better rejection against non-prompt leptons than the PLVTight working point with similar prompt efficiency.
    • The PLVTight working point was derived with the previous version of our algorithm called PromptLeptonVeto.
  • PLImprovedVeryTight was optimised to provide even stronger rejection of non-prompt leptons at the price of reduced prompt lepton efficiency.
  • The working points were then tuned to provide the continuous efficiency as function of prompt lepton pT.
  • The optimisation procedure was presented in the IFF meeting on October 19th, 2020.
    • Each working point was chosen from thousands of candidates in order to give a best overall significance for several benchmark analyses.
  • Working point definitions are documented in git: PLIVCutValue.
  • Working point efficiency for prompt and non-prompt leptons is documented in git: PLImprovedWPs

Required DxAOD variables

  • The required discriminant variable for muons is "PromptLeptonImprovedVeto".
  • The required discriminant variable for barrel region electrons is "PromptLeptonImprovedVetoBARR" and for endcap region electrons is "PromptLeptonImprovedVetoECAP".
  • The pT bin variable called "PromptLeptonImprovedInput_MVAXBin" is also necessary for applying correct pT-dependent cuts. This is because PromptLeptonImprovedVeto distributions are slightly different in different pT bins.
  • These variables are calculated at the derivation level by the LeptonTagger package.
  • Shown below are example configuration lines for adding the required variables to release 21.2 derivations:
import LeptonTaggers.LeptonTaggersConfig as LepTagConfig

# Add the PromptLeptonImprovedVeto algorithm to AthSequencer
MUON5Seq = CfgMgr.AthSequencer("MUON5Sequence")
MUON5Seq += LepTagConfig.GetDecorateImprovedPromptLeptonAlgs()

# Get the auxiliary variable list 
ExtraVariables += LepTagConfig.GetExtraImprovedPromptVariablesForDxAOD() # including all input variables and discriminant variable of the PromptLeptonImprovedVeto.
# ExtraVariables += LepTagConfig.GetExtraImprovedPromptVariablesForDxAOD(onlyBDT=True)  # only get the discriminant variable  and pT bin variable of electron and muon.

Working points usage

isoTool = CfgMgr.CP__IsolationSelectionTool('IsolationSelectionTool')
isoTool.MuonWP = "PLImprovedTight" # or "PLImprovedVeryTight"
isoTool.ElectronWP = "PLImprovedTight" # or "PLImprovedVeryTight"
ToolSvc += isoTool
isoLowPtPLVTool = CfgMgr.CP__IsolationLowPtPLVTool( 'IsolationLowPtPLVTool')
athTool = CfgMgr.Ath__IsoTool('Ath_IsoTool')
athTool.isoTool = isoTool
athTool.isoLowPtPLVTool = isoLowPtPLVTool
athTool.doPLV = False
athTool.varName = 'isoPLImprovedTight' # or  "isoPLImprovedVeryTight"
ToolSvc += athTool

Working point calibrations

  • Calibrations of electron and muon working points are ongoing
    • Initial calibrations for muons are available, and currently being checked and validated: MCP presentation on January 13th, 2021
    • Electron calibrations are also underway, currently planning for results in February or March of 2021
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