Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ

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XU Ye, HOU Jian and ZHU Kai-En. Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ[J]. Chinese Physics C, 2008, 32(3): 201-204. doi: 10.1088/1674-1137/32/3/008
XU Ye, HOU Jian and ZHU Kai-En. Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ[J]. Chinese Physics C, 2008, 32(3): 201-204.  doi: 10.1088/1674-1137/32/3/008 shu
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Received: 2007-05-28
Revised: 2007-06-14
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Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ

    Corresponding author: XU Ye,

Abstract: 

The Monte-Carlo samples of pion, kaon and proton generated from 0.3GeV/c to 1.2GeV/c by the `tester' generator from SIMBES which are used to simulate the detector of BESⅡ are identified with the Bayesian neural networks (BNN). The pion identification and misidentification efficiencies are obviously better at high momentum region using BNN than the methods of χ2 analysis of dE/dX and TOF information. The kaon identification and misidentification efficiencies are obviously better from 0.3GeV/c to 1.2GeV/c using BNN than the methods of χ2 analysis. The proton identification and misidentification efficiencies using BNN are basically consistent with the ones of χ2 analysis. The anti-proton identification and misidentification efficiencies are better below 0.6GeV/c using BNN than the methods of χ2 analysis.

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