摘要
In the LHC experiment, the neutral pions produced during jet fragmentation are the background sources for all physics channels with high-energy photons in their final state. In this paper, the application of the three-dimensional parametric formula for electromagnetic （EM） showers, which we developed in the Alpha Magnetic Spectrometer II experiment, is presented to distinguish the unconverted photons from the neutral pions. With the constructed electromagnetic calorimeter （ECAL） in a GEANT4 simulation, the parametric formulae were validated and the unconverted γ/π0 discrimination was performed with the Toolkit for Multivariate Data Analysis （TMVA） package in ROOT for different transverse energies ranging from 15 GeV to 75 GeV, which is the most sensitive region for light Higgs （with mass ～120 GeV） searches with the channel H → γγ. With this discrimination method and the selected transverse energy region, we can reject π0 with the effciency from ～ 40% （65–75 GeV） to ～ 90% （15–25 GeV） when keeping 90% γ effciency.
In the LHC experiment, the neutral pions produced during jet fragmentation are the background sources for all physics channels with high-energy photons in their final state. In this paper, the application of the three-dimensional parametric formula for electromagnetic （EM） showers, which we developed in the Alpha Magnetic Spectrometer II experiment, is presented to distinguish the unconverted photons from the neutral pions. With the constructed electromagnetic calorimeter （ECAL） in a GEANT4 simulation, the parametric formulae were validated and the unconverted γ/π0 discrimination was performed with the Toolkit for Multivariate Data Analysis （TMVA） package in ROOT for different transverse energies ranging from 15 GeV to 75 GeV, which is the most sensitive region for light Higgs （with mass ～120 GeV） searches with the channel H → γγ. With this discrimination method and the selected transverse energy region, we can reject π0 with the effciency from ～ 40% （65–75 GeV） to ～ 90% （15–25 GeV） when keeping 90% γ effciency.
基金
Supported by National Natural Science Foundation of China (10435070, 10721140381, 10099630)
China Ministry of Science and Technology (2007CB16101)
Chinese Academy of Sciences (KJCX2-N17, 1730911111)
