Speaker
Description
Machine learning has the potential to enhance the sensitivities of water Cherenkov detectors by improving the event reconstruction to suppress backgrounds and systematic uncertainties. Such improvements will be vital in achieving the precision measurements that current and next-generation water Cherenkov detectors are now aiming to perform.
This talk covers several areas where machine learning is being explored for event reconstruction in the Super-Kamiokande and Hyper-Kamiokande projects. Specific physics motivations are discussed, including applications to neutrino oscillation and astrophysical neutrino measurements, followed by an overview of the plans, progress, and challenges of ongoing efforts to use machine learning techniques in these areas.
