In simulated IWCD data, a ResNet-18 classifier selects electron neutrino events with 61.5% purity and 78.2% efficiency, improving on fiTQun's 51.1% and 69.5%.
Improving the t2k oscillation analysis with fitqun: a new maximum-likelihood event reconstruction for super-kamiokande
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Likelihood and Deep Learning Analysis of the electron neutrino event sample at Intermediate Water Cherenkov Detector (IWCD) of the Hyper-Kamiokande experiment
In simulated IWCD data, a ResNet-18 classifier selects electron neutrino events with 61.5% purity and 78.2% efficiency, improving on fiTQun's 51.1% and 69.5%.