Demonstrates background suppression in single electron signals using neural networks, indicating improved CEνNS event detection.
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.
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Razuvaeva et al. (2026) studied this question.
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