First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector

TL;DR

This paper introduces a new waveform analysis and machine learning-based method for reconstructing the direction of atmospheric neutrinos in large liquid scintillator detectors, enhancing their utility in neutrino oscillation studies.

Contribution

It presents the first successful demonstration of directionality reconstruction in large homogeneous liquid scintillator detectors using advanced analysis techniques.

Findings

Achieved good direction resolution for atmospheric neutrinos

Demonstrated potential for future neutrino oscillation measurements

Introduced a novel waveform and machine learning approach

Abstract

The directionality information of incoming neutrinos is essential to atmospheric neutrino oscillation analysis since it is directly related to the oscillation baseline length. Large homogeneous liquid scintillator detectors, while offering excellent energy resolution, are traditionally very limited in their capabilities of measuring event directionality. In this paper, we present a novel directionality reconstruction method for atmospheric neutrino events in large homogeneous liquid scintillator detectors based on waveform analysis and machine learning techniques. We demonstrate for the first time that such detectors can achieve good direction resolution and potentially play an important role in future atmospheric neutrino oscillation measurements.