Vertex and energy reconstruction in JUNO with machine learning methods
文献类型：期刊论文
| 作者 | Qian, Zhen; Belavin, Vladislav; Bokov, Vasily; Brugnera, Riccardo; Compagnucci, Alessandro; Gavrikov, Arsenii; Garfagnini, Alberto; Gonchar, Maxim; Khatbullina, Leyla; Li, Ziyuan |
| 刊名 | NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
 |
| 出版日期 | 2021 |
| 卷号 | 1010 |
| 关键词 | Machine learning JUNO Energy reconstruction Vertex reconstruction |
| ISSN号 | 0168-9002 |
| DOI | 10.1016/j.nima.2021.165527 |
| 文献子类 | Article |
| 英文摘要 | The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters sin(2) theta(12), Delta m(21)(2) and Delta(2)(31) are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present several machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. The models of BDT and DNN are trained with aggregated information, pre-calculated from PMT signal, while the others are trained with PMT-wise measured information from 17600 PMTs. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: sigma(E) = 3% at E-vis = 1 MeV for the energy and sigma(x,y,z) = 10 cm at E-vis = 1 MeV for the position. |
| 学科主题 | Instruments & Instrumentation ; Nuclear Science & Technology ; Physics |
| 电子版国际标准刊号 | 1872-9576 |
| 语种 | 英语 |
| WOS记录号 | WOS:000672431800011 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/297540]  |
| 专题 | 江门中微子实验 |
| 推荐引用方式 GB/T 7714 | Qian, Zhen,Belavin, Vladislav,Bokov, Vasily,et al. Vertex and energy reconstruction in JUNO with machine learning methods[J]. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT,2021,1010. |
| APA | Qian, Zhen.,Belavin, Vladislav.,Bokov, Vasily.,Brugnera, Riccardo.,Compagnucci, Alessandro.,...&Manzali, Francesco.(2021).Vertex and energy reconstruction in JUNO with machine learning methods.NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT,1010. |
| MLA | Qian, Zhen,et al."Vertex and energy reconstruction in JUNO with machine learning methods".NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 1010(2021). |
入库方式： OAI收割
来源：高能物理研究所
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