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Abstract <p>RED-100 is a two phase detector with liquid xenon as a target material designed to study coherent elastic neutrino nucleus scattering (CEνNS) of reactor antineutrino at Kalinin Nuclear Power Plant (KNPP). Signals from the coincidence of background several single ionization electrons (SE) are similar to signals from CEνNS events. This paper describes machine learning algorithms designed to suppress such a background.</p>

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Neural Networks for the Single Electrons Coincidence Background Mitigation in the RED-100 Detector

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • O. E. Razuvaeva,
  • D. G. Rudik,
  • V. A. Belov,
  • A. M. Konovalov,
  • E. S. Kozlova

摘要

Abstract

RED-100 is a two phase detector with liquid xenon as a target material designed to study coherent elastic neutrino nucleus scattering (CEνNS) of reactor antineutrino at Kalinin Nuclear Power Plant (KNPP). Signals from the coincidence of background several single ionization electrons (SE) are similar to signals from CEνNS events. This paper describes machine learning algorithms designed to suppress such a background.