Dataset for neutron and gamma-ray pulse shape discrimination

TL;DR

This paper introduces a comprehensive, publicly available dataset of neutron and gamma-ray pulse signals, along with source codes for various discrimination algorithms, enabling standardized evaluation and noise robustness testing.

Contribution

It provides the first extensive dataset with source codes for multiple pulse shape discrimination algorithms and evaluation tools for noise robustness in neutron and gamma-ray detection.

Findings

Dataset includes raw and noise-enhanced pulse signals.

Source codes for multiple discrimination algorithms are provided.

Tools for performance and anti-noise evaluation are included.

Abstract

The publicly accessible dataset includes neutron and gamma-ray pulse signals for conducting pulse shape discrimination experiments. Several traditional and recently proposed pulse shape discrimination algorithms are utilized to evaluate the performance of pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms comprise zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and het-erogeneous quasi-continuous spiking cortical model (HQC-SCM). In addition to the pulse signals, this dataset includes the source code for all the aforementioned pulse shape discrimination methods. Moreover, the dataset provides the source code for schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation. This feature enables researchers to evaluate the performance of these methods using standard procedures and assess their anti-noise ability under various noise conditions. In conclusion, this dataset offers a comprehensive set of resources for conducting pulse shape discrimination experiments and evaluating the performance of various pulse shape discrimination methods under different noise scenarios.