Physics-informed machine learning approaches to reactor antineutrino detection

TL;DR

This paper compares machine learning methods, including a tree-based classifier and a graph neural network, to improve background rejection in reactor antineutrino detection, enhancing sensitivity in water Cherenkov detectors.

Contribution

It introduces and evaluates two machine learning techniques for better background rejection in reactor antineutrino detection, demonstrating improvements over traditional methods.

Findings

Tree-based classifier improves background rejection.

Graph convolutional neural network enhances event classification.

Machine learning methods outperform traditional cut-and-count approaches.

Abstract

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.