Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks


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Authors
Griffiths, J 
Kleinegesse, S 
Saunders, D 
Taylor, R 
Abstract

jats:titleAbstract</jats:title> jats:pWe 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 silicon photomultiplier signals from a dual scintillator detector element made of jats:sup6</jats:sup>Li F: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 utilising a separate photomultiplier tube was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and a simpler artificial neural net. It is found to provide superior levels of discrimination, achieving an area under the curve of 0.996 ± 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.</jats:p>

Description
Keywords
machine learning, convolutional neural networks, pulse shape discrimination, Li-6 F:ZnS(Ag), t-SNE
Journal Title
Machine Learning: Science and Technology
Conference Name
Journal ISSN
2632-2153
2632-2153
Volume Title
1
Publisher
IOP Publishing
Sponsorship
H2020 European Research Council (682474)