2024/07/08 by S. Farrell, M. Bergevin, Farrell, Sophia +3
Engineering · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Fault Detection and Control Systems #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det) #Nuclear Physics and Applications #Nuclear reactor physics and engineering #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2407.06139
openalex publication_date 2024/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Nuclear reactors produce a high flux of MeV-scale antineutrinos that can be observed through inverse beta-decay (IBD) interactions in particle detectors. Reliable detection of reactor IBD signals depends on suppression of backgrounds, both by physical shielding and vetoing and by pattern recognition and rejection in acquired data. A particularly challenging background to reactor antineutrino detection is from cosmogenically induced fast neutrons, which can mimic the characteristics of an IBD signal. In this work, we explore two methods of machine learning -- a tree-based classifier and a graph-convolutional neural network -- to improve rejection of fast neutron-induced background events in a water Cherenkov detector. The tree-based classifier examines classification at the reconstructed feature level, while the graphical network classifies events using only the raw signal data. Both methods improve the sensitivity for a background-dominant search over traditional cut-and-count methods, with the greatest improvement being from the tree-based classification method. These performance enhancements are relevant for reactor monitoring applications that make use of deep underground oil-based or water-based kiloton-scale detectors with multichannel, PMT-based readouts, and they are likely extensible to other similar physics analyses using this class of detector.