Maximum likelihood reconstruction of water Cherenkov events with deep generative neural networks

Autor: Jia, Mo, Kumar, Karan, Mackey, Liam S., Putra, Alexander, Vilela, Cristovao, Wilking, Michael J., Xia, Junjie, Yanagisawa, Chiaki, Yang, Karan

Large water Cherenkov detectors have shaped our current knowledge of neutrino physics and nucleon decay, and will continue to do so in the foreseeable future. These highly capable detectors allow for directional and topological, as well as calorimetr

Externí odkaz: http://arxiv.org/abs/2202.01276

Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks.

Autor: Jia M; Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, United States., Kumar K; Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, United States., Mackey LS; Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, United States., Putra A; Department of Mathematics, BMCC/City University of New York, New York, NY, United States., Vilela C; CERN European Organization for Nuclear Research, Geneva, Switzerland., Wilking MJ; Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, United States., Xia J; Institute for Cosmic Ray Research, Research Center for Cosmic Neutrinos, University of Tokyo, Chiba, Japan., Yanagisawa C; Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, United States.; Department of Science, BMCC/City University of New York, New York, NY, United States., Yang K; Information Science, Cornell Tech, New York, NY, United States.

Publikováno v: Frontiers in big data [Front Big Data] 2022 Jun 17; Vol. 5, pp. 868333. Date of Electronic Publication: 2022 Jun 17 (Print Publication: 2022).