Pulse Shape Discrimination and Exploration of Scintillation Signals Using Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks can effectively discriminate neutron and gamma signals from raw scintillation data, outperforming traditional methods and revealing underlying signal structures.

Contribution

The study introduces a CNN-based approach for pulse shape discrimination using raw detector signals, achieving higher accuracy and uncovering new data insights.

Findings

CNN achieves AUC of 0.995 in discrimination

Method is more effective than charge integration and wavelet transform

Reveals substructure in signals beyond original labels

Abstract

We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised SiPM signals from a dual scintillator detector element made of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilsing a separate PMT was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and is found to provide superior levels of discrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods.