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

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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.