Particle identification at MeV energies in JUNO
文献类型：期刊论文
| 作者 | Rebber, H; Ludhova, L; Wonsaka, B; Xu, Y |
| 刊名 | JOURNAL OF INSTRUMENTATION
 |
| 出版日期 | 2021 |
| 卷号 | 16期号:1 |
| 关键词 | Neutrino detectors Particle identification methods Scintillators, scintillation and light emission processes (solid, gas and liquid scintillators) |
| DOI | 10.1088/1748-0221/16/01/P01016 |
| 文献子类 | Article |
| 英文摘要 | JUNO is a multi-purpose neutrino experiment currently under construction in Jiangmen, China. It is primarily aiming to determine the neutrino mass ordering. Moreover, its 20 kt target mass makes it an ideal detector to study neutrinos from various sources, including nuclear reactors, the Earth and its atmosphere, the Sun, and even supernovae. Due to the small cross section of neutrino interactions, the event rate of neutrino experiments is limited. In order to maximize the signal-to-noise ratio, it is extremely important to control the background levels. In this paper we discuss the potential of particle identification in JUNO, its underlying principles and possible areas of application in the experiment. While the presented concepts can be transferred to any large liquid scintillator detector, our methods are evaluated specifically for JUNO and the results are mainly driven by its high optical photon yield of 1,200 photo electrons per MeV of deposited energy. In order to investigate the potential of event discrimination, several event pairings are analysed, i.e alpha/beta, p/beta, e(+)/e(-) and e(-)/gamma. We compare the discrimination performance of advanced analytical techniques based on neural networks and on the topological event reconstruction keeping the standard Gatti filter as a reference. We use the Monte Carlo samples generated in the physically motivated energy intervals. We study the dependence of our cuts on energy, radial position, PMT time resolution, and dark noise. The results show an excellent performance for alpha/beta and p/beta with the Gatti method and the neural network. Furthermore, e(+)/e(-) and e(-)/gamma can partly be distinguished by means of neural network and topological reconstruction on a statistical basis. Especially in the latter case, the topological method proved very successful. |
| 语种 | 英语 |
| WOS记录号 | WOS:000663343100023 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/297560]  |
| 专题 | 江门中微子实验 |
| 推荐引用方式 GB/T 7714 | Rebber, H,Ludhova, L,Wonsaka, B,et al. Particle identification at MeV energies in JUNO[J]. JOURNAL OF INSTRUMENTATION,2021,16(1). |
| APA | Rebber, H,Ludhova, L,Wonsaka, B,&Xu, Y.(2021).Particle identification at MeV energies in JUNO.JOURNAL OF INSTRUMENTATION,16(1). |
| MLA | Rebber, H,et al."Particle identification at MeV energies in JUNO".JOURNAL OF INSTRUMENTATION 16.1(2021). |
入库方式： OAI收割
来源：高能物理研究所
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