Towards energy-insensitive and robust neutron/gamma classification: A learning-based frequency-domain parametric approach

TL;DR

This paper introduces a frequency-domain parametric modeling approach for neutron/gamma discrimination that improves accuracy and robustness, especially under varying data conditions, and is suitable for real-time, resource-constrained applications.

Contribution

It proposes a novel frequency-domain parametric modeling framework with tunable parameters, enhancing discrimination accuracy and robustness over traditional methods.

Findings

Higher accuracy in neutron/gamma classification.

Better adaptability to noise and sampling variations.

Suitable for online, low-resource hardware implementation.

Abstract

Neutron/gamma discrimination has been intensively researched in recent years, due to its unique scientific value and widespread applications. With the advancement of detection materials and algorithms, nowadays we can achieve fairly good discrimination. However, further improvements rely on better utilization of detector raw signals, especially energy-independent pulse characteristics. We begin by discussing why figure-of-merit (FoM) is not a comprehensive criterion for high-precision neutron/gamma discriminators, and proposing a new evaluation method based on adversarial sampling. Inspired by frequency-domain analysis in existing literature, parametric linear/nonlinear models with minimum complexity are created, upon the discrete spectrum, with tunable parameters just as neural networks. We train the models on an open-source neutron/gamma dataset (CLYC crystals with silicon photomultipliers) preprocessed by charge normalization to discover and exploit energy-independent features. The performance is evaluated on different sampling rates and noise levels, in comparison with the frequency classification index and conventional methods. The frequency-domain parametric models show higher accuracy and better adaptability to variations of data integrity than other discriminators. The proposed method is also promising for online inference on economical hardware and portable devices.