Novel Machine Learning-Based Event Reconstruction and Analysis for the Water Cherenkov Experiment

基于机器学习的新型水切伦科夫实验事件重建和分析

• 批准号: 22KF0113
• 负责人: ヴァギンズ マーク
• 金额: $ 1.41万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2023
• 资助国家: 日本
• 起止时间: 2023-03-08 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22KF0113/
• 关键词: Deep learning; Neural networks; Water Cherenkov; Neutrino; Particle Physics; Cosmology

I have modified the pipeline of current deep learning based water Cherenkov event generative neural network (CRinGe, arXiv:2202.01276v1). Thus the computational speed is improved by a factor of 5 and the training process can be finished within days instead of weeks. Different architectures and loss functions have been implemented for this neural network to achieve better numerical stability and physical robustness. In the meantime I am developing the Monte-Carlo simulation and analysis pipeline for the upcoming Water Cherenkov Test Experiment in CERN, for which the first test run will start in July 2023.Collaborating with M.Mandal from Poland National Centre for Nuclear Research, I have developed, verified, and implemented a new event selection criterion for the T2K and Super-Kamiokande-T2K (SK-T2K) joint neutrino oscillation analysis to exclude the potential neutron background events in the new SK detector with Gadolinium. We constrained the impurity in the selected events to less than 1%. Meanwhile I am also fulfilling my responsibility of updating and maintaining the T2K data taking and reduction pipeline to adapt to the new SK detector and more powerful T2K neutrino beam.Besides, I have established a collaboration with the cosmologists from both domestic and foreign institutions to investigate the possibility of common deep learning techniques for particle physics and cosmology research. Taking this chance I have contributed to the foundation and inauguration of the new Center of Data-Driven Discovery (CD3, https://cd3.ipmu.jp/) at Kavli IPMU, the University of Tokyo.

我修改了当前基于深度学习的水切伦科夫事件生成神经网络（CRinGe，arXiv：2202.01276v1）的流程。因此，计算速度提高了 5 倍，并且训练过程可以在几天而不是几周内完成。该神经网络采用了不同的架构和损失函数，以实现更好的数值稳定性和物理鲁棒性。与此同时，我正在为 CERN 即将进行的水切伦科夫测试实验开发蒙特卡罗模拟和分析管道，首次测试将于 2023 年 7 月开始。与波兰国家核研究中心的 M.Mandal 合作，我开发、验证和实施了 T2K 和 Super-Kamiokande-T2K (SK-T2K) 联合中微子振荡分析的新事件选择标准，以排除新中子背景事件带钆的 SK 探测器。我们将所选事件中的杂质限制在 1% 以下。同时我也在履行更新和维护T2K数据采集和缩减流程的责任，以适应新的SK探测器和更强大的T2K中微子束。此外，我还与国内外机构的宇宙学家建立了合作关系来研究粒子物理和宇宙学研究中通用深度学习技术的可能性。借此机会，我为东京大学 Kavli IPMU 的新数据驱动发现中心（CD3，https://cd3.ipmu.jp/）的成立和落成做出了贡献。

项目成果

The Current Status of T2K

T2K的现状

• 发表时间: 2022
• 作者: Junjie Xia

A Generative Convolutional Neural Network Approach for Cherenkov Event Reconstruction (arXiv:2202.01276v1)

用于切伦科夫事件重建的生成卷积神经网络方法 (arXiv:2202.01276v1)

• 发表时间: 2023
• 作者: Junjie Xia

T2K-SK joint nu oscillation sensitivity

T2K-SK关节nu振荡灵敏度

• DOI: 10.22323/1.421.0008
• 发表时间: 2023
• 作者: Xia Junjie;Barrow Daniel;Berns Lukas;Blanchet Adrien;Bronner Christophe;Friend Megan;Guigue M.;Hu J.;Jia M.;Jiang J.;Shi W.;Wilking Michael;Wendell Roger;Wret Clarence;Xie Z.;T2K collaboration;Super;e Collaboration
• 通讯作者: e Collaboration

Sensitivity Studies for A Joint Oscillation Analysis of SK Atmospheric and T2K Accelerator Neutrinos

SK 大气和 T2K 加速器中微子联合振荡分析的灵敏度研究

• 发表时间: 2022
• 作者: Junjie Xia

A Generative Convolutional Neural Network Approach for Cherenkov Event Reconstruction (arXiv:2202.01276v1)

用于切伦科夫事件重建的生成卷积神经网络方法 (arXiv:2202.01276v1)

• 发表时间: 2022
• 作者: Junjie Xia