11 citations · 30 across the 6 of their papers we have counts for
6 papers
Search for the Decay Mode in Super-Kamiokande Using Machine Learning Techniques
Kamiokande Collaboration, J. Feng, K. Abe +296
We report a new partial lifetime limit of years for the trinucleon decay mode , obtained from a searc…
Enhancing Event Reconstruction in Hyper-Kamiokande with Machine Learning: A ResNet Implementation
Andrew Atta, Nick Prouse, Shuoyu Chen +6
The forthcoming Hyper-Kamiokande experiment is expected to achieve unprecedented sensitivity to neutrino oscillations. Meeting these precision goals requires substantially larger M…
Neutron Tagging following Atmospheric Neutrino Events in a Water Cherenkov Detector
K. Abe, Y. Haga, Y. Hayato +301
We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimate…
Using Machine Learning to Improve Neutron Identification in Water Cherenkov Detectors
Blair Jamieson, Matt Stubbs, Sheela Ramanna +5
Water Cherenkov detectors like Super-Kamiokande, and the next generation Hyper-Kamiokande are adding gadolinium to their water to improve the detection of neutrons. By detecting ne…
New Methods and Simulations for Cosmogenic Induced Spallation Removal in Super-Kamiokande-IV
Kamiokande Collaboration, S. Locke, A. Coffani +215
Radioactivity induced by cosmic muon spallation is a dominant source of backgrounds for MeV neutrino interactions in water Cherenkov detectors. In particular, it…
Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors
Abhishek Abhishek, Wojciech Fedorko, Patrick de Perio +2
Matter-antimatter asymmetry is one of the major unsolved problems in physics that can be probed through precision measurements of charge-parity symmetry violation at current and ne…