Applying Bayesian neural networks to identify pion, kaon and proton in BESII**Supported by National Natural Science Foundation of China (10605014)

Abstract

The Monte-Carlo samples of pion, kaon and proton generated from 0.3 GeV/c to 1.2 GeV/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 0.3 GeV/c to 1.2 GeV/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 0.6 GeV/c using BNN than the methods of χ2 analysis.