Electron Identification using Neural Network

Complete: 1


The electron identification with Neural Network (NN) is a standard CMS tool for electron ID along with cut-based electron ID and likelihood-based electron ID. To limit the complexity of the network, it uses only a minimal set of variables to perform the electron identification. The following variables are currently used to discriminate between real and fake electrons in the NN:
  • (1/E - 1/P)
  • Ratio of energy in HCAL behind SuperCluster to SuperCluster energy
  • DeltaPhi between SuperCluster position and track direction at vertex
  • SigmaEtaEta cluster shape covariance

Network architecture

The chosen NN architecture was 2N,N and was trained with 2500 events for both real and fake electrons. There are two weight files for each trained NN: one for the barrel and one for the endcap (note: at the moment of writing, endcap weight file is only a copy of barrel one). The weight filename structure is: weights_SAMPLE_{barrel,endcap}.txt


  • signal: sample of real electrons coming from Z->ee events
  • background: sample of fake electrons coming from Z+jet events with Z->mumu
In both samples, event are selected if they fullfil a requirement on the di-lepton invariant mass to be within a window of +11 GeV and -11 GeV around the Z mass. Moreover, in the signal sample real electrons have been selected using the Tag&Probe algorithm, while fake electrons were all the electrons reconstructed in the background sample.

Technical Implementation

We used TMVA (http://tmva.sf.net), a framework that allows to use MultiVariate Analysis tools in a ROOT-enabled environment.

Data Samples


  • NN architecture: 2N,N
  • Training Events: 2500 for both real and fake
  • Samples used: /ZmumuJets_Pt_50_80/CMSSW_1_3_1­Spring07­1204/GEN­SIM­DIGI­RECO, /Zee/CMSSW_1_3_1­Spring07­1353/GEN­SIM­DIGI­RECO
  • Files:


Setup of the code

compilation in 200preX

compilation in 180preX

-- LeonardoSala - 12 Feb 2008

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Topic revision: r6 - 2009-03-03 - MatteoSani
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