An artificial neural network for proton identification in HERMES data

• Published in: Chinese physics C Vol. 33; no. 3; pp. 217 - 223
• Main Author: 王思广 冒亚军 叶红学
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.03.2009; School of Physics and State Key Laboratory of Nuclear Physics & Technology, Peking University, Beijing 100871,China
• Subjects: TOF系统; 人工神经网络; 粒子识别; 质子识别; artificial neural network; particle identification; TOF
• ISSN: 1674-1137; 0254-3052; 2058-6132
• DOI: 10.1088/1674-1137/33/3/011

The HERMES time-of-flight （TOF） system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network （ANN） trained to recognize protons from ∧^0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron （assigned one proton mass） and a negative hadron （assigned one π^- mass） the reconstructed invariant mass lies within the ∧^0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero. The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.