Using Machine Learning to Improve Neutron Identification in Water Cherenkov Detectors

TL;DR

This paper employs machine learning models like XGBoost, GCN, and DGCNN to enhance neutron detection in water Cherenkov detectors, significantly improving classification accuracy and providing interpretability insights.

Contribution

It introduces the application of advanced ML models to optimize neutron identification in water Cherenkov detectors, outperforming traditional statistical methods.

Findings

Up to 10% increase in classification accuracy.

Effective feature engineering with SHAP analysis.

Benchmarking of ML models against traditional approaches.

Abstract

Water Cherenkov detectors like Super-Kamiokande, and the next generation Hyper-Kamiokande are adding gadolinium to their water to improve the detection of neutrons. By detecting neutrons in addition to the leptons in neutrino interactions, an improved separation between neutrino and anti-neutrinos, and reduced backgrounds for proton decay searches can be expected. The neutron signal itself is still small and can be confused with muon spallation and other background sources. In this paper, machine learning techniques are employed to optimize the neutron capture detection capability in the new intermediate water Cherenkov detector (IWCD) for Hyper-K. In particular, boosted decision tree (XGBoost), graph convolutional network (GCN), and dynamic graph convolutional neural network (DGCNN) models are developed and benchmarked against a statistical likelihood-based approach, achieving up to a 10% increase in classification accuracy. Characteristic features are also engineered from the datasets and analyzed using SHAP (SHapley Additive exPlanations) to provide insight into the pivotal factors influencing event type outcomes. The dataset used in this research consisted of roughly 1.6 million simulated particle gun events, divided nearly evenly between neutron capture and a background electron source.