# Machine Learning-based Energy Reconstruction for Water-Cherenkov detectors

## TL;DR

This paper introduces machine learning techniques to improve energy reconstruction of charged particles in water Cherenkov detectors, specifically applied to the TITUS detector design for Hyper-Kamiokande, enhancing neutrino physics measurements.

## Contribution

It presents novel machine learning-based methods for energy reconstruction in water Cherenkov detectors, demonstrating their effectiveness on the TITUS detector configuration.

## Key findings

- Machine learning methods outperform traditional reconstruction techniques.
- Enhanced energy resolution for charged particles in water Cherenkov detectors.
- Potential for improved neutrino flux measurements and physics analysis.

## Abstract

Hyper-Kamiokande (Hyper-K) is a proposed next generation underground water Cherenkov (WCh) experiment. The far detector will measure the oscillated neutrino flux from the long-baseline neutrino experiment using 0.6 GeV neutrinos produced by a 1.3 MW proton beam at J-PARC. It has a broad program of physics and astrophysics mainly focusing on the precise measurement of the lepton neutrino mixing matrix and the CP asymmetry. The unoscillated neutrino flux will be measured by an intermediate WCh detector. One of the proposed designs is the Tokai Intermediate Tank for the Unoscillated Spectrum (TITUS). WCh detectors are instrumented with photomultipliers to detect the Cherenkov light emitted from charged particles which are produced by neutrino interactions. The detection of light is used to measure the energy, position and direction of the charged particles. We propose machine learning-based methods to reconstruct the energy of charged particles in WCh detectors and present our results for the TITUS configuration.