An artificial neural network for proton identification in HERMES data

WANG Si-Guang; MAO Ya-Jun; YE Hong-Xue

doi:10.1088/1674-1137/33/3/011

近期发现有不法分子冒充我刊与作者联系，借此进行欺诈等不法行为，请广大作者加以鉴别，如遇诈骗行为，请第一时间与我刊编辑部联系确认（《中国物理C》（英文）编辑部电话：010-88235947,010-88236950），并作报警处理。

本刊再次郑重声明：

（1）本刊官方网址为cpc.ihep.ac.cn和https://iopscience.iop.org/journal/1674-1137

（2）本刊采编系统作者中心是投稿的唯一路径，该系统为ScholarOne远程稿件采编系统，仅在本刊投稿网网址（https://mc03.manuscriptcentral.com/cpc）设有登录入口。本刊不接受其他方式的投稿，如打印稿投稿、E-mail信箱投稿等，若以此种方式接收投稿均为假冒。

（3）所有投稿均需经过严格的同行评议、编辑加工后方可发表，本刊不存在所谓的“编辑部内部征稿”。如果有人以“编辑部内部人员”名义帮助作者发稿，并收取发表费用，均为假冒。

《中国物理C》（英文）编辑部

2024年10月30日

Chinese Physics C> 2009, Vol. 33> Issue(3) : 217-223 DOI: 10.1088/1674-1137/33/3/011

An artificial neural network for proton identification in HERMES data

Abstract
HTML
Reference
Related

PDF

Abstract：
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 Λ⁰ 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 Λ⁰ 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.

[1]	Yu Lu , Yi-Jia Wang , Ying Chen , Jia-Jun Wu . Rediscovery of numerical Lüscher's formula from the neural network. Chinese Physics C, 2024, 48(7): 073101. doi: 10.1088/1674-1137/ad3b9c
[2]	D. M. Habashy , Mahmoud Y. El-Bakry , Werner Scheinast , Mahmoud Hanafy . Entropy per Rapidity in Pb-Pb Central Collisions using Thermal and Artificial Neural Network (ANN) Models at LHC Energies. Chinese Physics C, 2022, 46(7): 073103. doi: 10.1088/1674-1137/ac5f9d
[3]	Chao Jin , Song-zhan Chen , Hui-hai He , (for the LHAASO Collaboration) . Classifying cosmic-ray proton and light groups in LHAASO-KM2A experiment with graph neural network. Chinese Physics C, 2020, 44(6): 065002. doi: 10.1088/1674-1137/44/6/065002
[4]	Chun-Wang Ma , Dan Peng , Hui-Ling Wei , Zhong-Ming Niu , Yu-Ting Wang , R. Wada . Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method. Chinese Physics C, 2020, 44(1): 014104. doi: 10.1088/1674-1137/44/1/014104
[5]	Chun-Wang Ma , Dan Peng , Hui-Ling Wei , Yu-Ting Wang , Jie Pu . A Bayesian-neural-network prediction for fragment production in proton induced spallation reaction. Chinese Physics C, 2020, 44(12): 124107. doi: 10.1088/1674-1137/abb657
[6]	Masoumeh Mohamadian , Hossein Afarideh , Mitra Ghergherehchi . Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling. Chinese Physics C, 2017, 41(1): 017003. doi: 10.1088/1674-1137/41/1/017003
[7]	Kai Xi , Chao Geng , Zhan-Gang Zhang , Ming-Dong Hou , You-Mei Sun , Jie Luo , Tian-Qi Liu , Bin Wang , Bing Ye , Ya-Nan Yin , Jie Liu . Monte Carlo predictions of proton SEE cross-sections from heavy ion test data. Chinese Physics C, 2016, 40(6): 066001. doi: 10.1088/1674-1137/40/6/066001
[8]	Cai-Xun Zhang , Shin-Ted Lin , Jian-Ling Zhao , Xun-Zhen Yu , Li Wang , Jing-Jun Zhu , Hao-Yang Xing . Discrimination of neutrons and γ-rays in liquid scintillator based on Elman neural network. Chinese Physics C, 2016, 40(8): 086204. doi: 10.1088/1674-1137/40/8/086204
[9]	H. E. Jackson (for the HERMES collaboration) . Recent results from the HERMES experiment. Chinese Physics C, 2010, 34(9): 1247-1253. doi: 10.1088/1674-1137/34/9/018
[10]	YU Wei-Lin (on behalf of the HERMES collaboration) . Deeply virtual compton scattering at HERMES. Chinese Physics C, 2010, 34(9): 1476-1478. doi: 10.1088/1674-1137/34/9/076
[11]	Eduard Avetisyan (on behalf of HERMES collaboration) . Exclusive meson production at HERMES. Chinese Physics C, 2010, 34(9): 1472-1475. doi: 10.1088/1674-1137/34/9/075
[12]	, , . Particle Identification using Artificial Neural Networks at BESIII. Chinese Physics C, 2008, 32(1): 1-8. doi: 10.1088/1674-1137/32/1/001
[13]	XU Ye , HOU Jian , ZHU Kai-En . Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ. Chinese Physics C, 2008, 32(3): 201-204. doi: 10.1088/1674-1137/32/3/008
[14]	YU Wei-Lin , MAO Ya-Jun , LU Xiao-Rui , MA Bo-Qiang . Study of the Reconstruction Methods in Searching for the Pentaquark at the Hermes. Chinese Physics C, 2005, 29(9): 847-851.
[15]	MAO Ya-Jun . Search for the Pentaquark at the HERMES. Chinese Physics C, 2004, 28(12): 1332-1335.
[16]	ZHANG Kun-Shi , LIU Lian-Shou . On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method. Chinese Physics C, 2004, 28(11): 1141-1145.
[17]	Zhang Ziping , Chen Hongfang , Ye Shuwei , Zhao Jiawei . Identification of e,μ,π by Neural Network in BES. Chinese Physics C, 1997, 21(4): 297-303.
[18]	Chen Guoming , Chen Gang . Identifying Tau Hadronic Decay with Neural Network. Chinese Physics C, 1995, 19(8): 692-702.
[19]	Chen Guoming , Chen Gang . Identifying τ Hadronic Decay with Neutral Network. Chinese Physics C, 1995, 19(S3): 247-256.
[20]	Zhang Li , Jin Genming , Zhao Jinhua , Yang Weifan , Yang Yongfeng , Zhao Zhizheng , Zheng Jiwen , Sun Xiurong , Wang Jicheng , Li Zongwei , Qin Zhi , Guo Guanghui . Identification of ²⁰⁸Hg. Chinese Physics C, 1994, 18(Z1): 1-8.

Access

Get Citation

WANG Si-Guang, MAO Ya-Jun and YE Hong-Xue. An artificial neural network for proton identification in HERMES data[J]. Chinese Physics C, 2009, 33(3): 217-223. doi: 10.1088/1674-1137/33/3/011

WANG Si-Guang, MAO Ya-Jun and YE Hong-Xue. An artificial neural network for proton identification in HERMES data[J]. Chinese Physics C, 2009, 33(3): 217-223. doi: 10.1088/1674-1137/33/3/011 shu

RIS(for EndNote,Reference Manager,ProCite)

BibTex

Txt

Milestone

Received: 2008-07-03
Revised: 2008-07-30

Article Metric

Article Views(4144)
PDF Downloads(674)
Cited by(0)

Policy on re-use

To reuse of subscription content published by CPC, the users need to request permission from CPC, unless the content was published under an Open Access license which automatically permits that type of reuse.

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

An artificial neural network for proton identification in HERMES data

Corresponding author: WANG Si-Guang,

Received Date: 2008-07-03
Accepted Date: 2008-07-30
Available Online: 2009-03-05

Abstract:

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 Λ⁰ 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 Λ⁰ 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.