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

我对目前基于深度学习的水Cherenkov事件生成神经网络（CRinGe, arXiv:2202.01276v1）的管道进行了修改。因此，计算速度提高了5倍，训练过程可以在几天内完成，而不是几周。为了获得更好的数值稳定性和物理鲁棒性，该神经网络采用了不同的结构和损失函数。与此同时，我正在为即将在欧洲核子研究中心进行的水切伦科夫测试实验开发蒙特卡洛模拟和分析管道，第一次测试运行将于2023年7月开始。我与波兰国家核研究中心的M.Mandal合作，开发、验证并实施了T2K和超级神冈-T2K （SK-T2K）联合中微子振荡分析的新事件选择标准，以排除新型钆SK探测器中潜在的中子背景事件。我们将所选事件中的杂质限制在小于1%。同时，我也在履行我的职责，更新和维护T2K数据采集和约简管道，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，探讨粒子物理和宇宙学研究中常见的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/）的建立和落成做出了贡献。

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.