Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector

2025/03/27 by Liu, Jiaxi, Zeng, Fanrui, Duyang, Hongyue +10 · 1 citation
#FOS: Physical sciences #High Energy Physics - Experiment (hep-ex)

paper · doi:10.48550/arxiv.2503.21353

Abstract

Atmospheric neutrino oscillations are important to the study of neutrino properties, including the neutrino mass ordering problem. A good capability to identify neutrinos' flavor and neutrinos against antineutrinos is crucial in such measurements. In this paper, we present a machine-learning-based approach for identifying atmospheric neutrino events in a large homogeneous liquid scintillator detector. This method identifies features of PMT waveforms that reflect event topologies and uses them as input to machine learning models. In addition, neutron-capture information is utilized to achieve neutrino versus antineutrino discrimination. Preliminary performances based on Monte Carlo simulations are presented, which demonstrate such a detector's potential in future measurements of atmospheric neutrinos such as the one planned for the JUNO experiment.

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