Likelihood and Deep Learning Analysis of the electron neutrino event sample at Intermediate Water Cherenkov Detector (IWCD) of the Hyper-Kamiokande experiment

Hyper-Kamiokande (Hyper-K) is a next-generation long baseline neutrino experiment. One of its primary physics goals is to measure neutrino oscillation parameters precisely, including the Dirac CP violating phase. As conventional ${\nu }_{\mu }$ beam generates from the J-PARC neutrino baseline contains only 1.5$\mathrm{\%}$ of ${\nu }_e$ interaction of total, it is challenging to measure ${\nu }_e\mathrm{/}{\bar{\nu }}_e$ scattering cross-section on nuclei. To reduce these systematic uncertainties, IWCD will be built to study neutrino interaction rates with higher precision. Simulated data comprise ${\nu }_{e}CC0\pi$ as the main signal with NC${\pi }^0$ and ${\nu }_{\mu }CC$ are major background events. To reduce the backgrounds initially, a log-likelihood-based reconstruction algorithm to select candidate events was used. However, this method sometimes struggles to distinguish ${\pi }^0$ events properly from electron-like events. Thus, a Machine Learning-based framework has been developed and implemented to enhance the purity and efficiency of ${\nu }_e$ events.