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

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 BES Ⅱ 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 χ^2 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 X2 analysis. The proton identification and misidentification efficiencies using BNN are basically consistent with the ones of χ^2 analysis. The anti-proton identification and misidentification efficiencies are better below 0.6 GeV/c using BNN than the methods of χ^2 analysis.

北京谱仪(BESⅡ)上p和■鉴别的研究

以北京谱仪(BES)上常用的3种粒子鉴别方法为基础,充分利用BESⅡ所获取的R值扫描、J/ψ和ψ′数据中p,(?)以及其他带电粒子样本,对动量范围在0.3—1.2GeV的p和(?)鉴别方法进行分析研究,找到BESⅡ上p和(?)的最佳鉴别方法:动量低于0.6GeV只用dE/dx实现粒子鉴别,动量高于0.6GeV可用TOF或联合鉴别的方法,并给出p,(?)的鉴别效率以及本底的混入比例。

p,在北京谱仪(Ⅱ)中的行为研究

从MonteCalor模拟和北京谱仪 (BESⅡ )所取的实验数据入手 ,分析了p和 p产生次级带电径迹和中性径迹的几率以及好径迹选择条件对这些次级径迹的去除情况、p在探测器中的能量损失和 p的湮没、p和 p在桶部簇射计数器中沉积能量的分布以及 3种不同的粒子鉴别方法的优劣 .给出了BES目前可以正确重建出的p和 p的动量的最小值和利用TOF和dE dx的联合信息鉴别p和 p的优越性 .