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

Jiaxi Liu1,2,*, Fanrui Zeng3,*, Hongyue Duyang3,†, Wanlei Guo1, Xinhai He1, Teng Li3,‡, Zhen Liu1,§, Wuming Luo1,∥, Wing Yan Ma3,¶ et al.

Xiaohan Tan3, Liangjian Wen1, Zekun Yang3, and Yongpeng Zhang1,**

  • *J. L. and F. Z. contributed equally to this work.
  • †Contact author: duyang@sdu.edu.cn
  • ‡Contact author: tengli@sdu.edu.cn
  • §Contact author: liuzhen@ihep.ac.cn
  • ∥Contact author: luowm@ihep.ac.cn
  • Contact author: wingyanma@sdu.edu.cn
  • **Contact author: ypzhang1991@ihep.ac.cn
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Phys. Rev. D 112, 012018 – Published 22 July, 2025

DOI: https://doi.org/10.1103/fznt-z257

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 photomultiplier tube waveforms that reflect event topologies and uses them as input to machine learning models. In addition, neutron-capture information is utilized to achieve neutrino vs 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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Phys. Rev. D 112, 012018– Published 22 July, 2025
  • Received 29 March 2025
  • Accepted 20 June 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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