Suppression of Cosmic Muon Spallation Backgrounds in Liquid Scintillator Detectors Using Convolutional Neural Networks

Abstract

Cosmic muon spallation backgrounds are ubiquitous in low-background experiments. For liquid scintillator-based experiments searching for neutrinoless double-beta decay, the spallation product ${}^{10}$C is an important background in the region of interest between 2-3 MeV and determines the depth requirement for the experiment. We have developed an algorithm based on a convolutional neural network that uses the temporal and spatial correlations in light emissions to identify ${}^{10}$C background events. With a typical kiloton-scale detector configuration like the KamLAND detector, we find that the algorithm is capable of identifying 61.6% of the ${}^{10}$C at 90% signal acceptance. A detector with perfect light collection could identify 98.2% at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources.

arXiv comment19 pages, 8 figures

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• KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event Search in KamLAND-Zen
• Air-Transfer Production Method for Large-Area Picosecond Photodetectors
• Space-Time Discriminant to Separate Double-Beta Decay from ${}^8$B Solar Neutrinos in Liquid Scintillator
• Search for Hidden Neutrinos at the European Spallation Source: the SHiNESS experiment
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• New Spallation Background Rejection Techniques to Greatly Improve the Solar Neutrino Sensitivity of JUNO
• Towards First Detection of the Solar MSW Transition With JUNO