2020
DOI: 10.1088/2632-2153/abb781
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Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks

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 silicon photomultiplier signals from a dual scintillator detector element made of 6Li 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 signa… Show more

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Cited by 32 publications
(13 citation statements)
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“…These approaches can then be extended to other machine learning techniques, such as Convolutional Neural Networks (CNNs), to extract the features embedded in each waveform to better discriminate between nuclear and electron recoil events. CNNs have seen use in discrimination between neutron and gamma pulse shapes previously [21], but this approach is reliant on greater statistics for network training.…”
Section: Discussionmentioning
confidence: 99%
“…These approaches can then be extended to other machine learning techniques, such as Convolutional Neural Networks (CNNs), to extract the features embedded in each waveform to better discriminate between nuclear and electron recoil events. CNNs have seen use in discrimination between neutron and gamma pulse shapes previously [21], but this approach is reliant on greater statistics for network training.…”
Section: Discussionmentioning
confidence: 99%
“…Recently, deep learning [13] as a renewed machine learning technique has progressed rapidly. It has been successfully used for particle/event discrimination and identification at the pulse level [14], the pixel level [15] and the voxel (three-dimensional) level [16]. In view of the fact that neural networks are applicable to classification tasks as well as regression tasks, it is meaningful to explore the capability of deep learning in the above-mentioned pulse timing problem.…”
Section: Introductionmentioning
confidence: 99%
“…Pulses are identified by the classification algorithms based on their characteristic geometrical features. Some analyses use the raw shape of the pulse directly to identify the pulse (e.g., using convolutional neural networks [28] or deep learning [29]) but typically an intermediate parameterization step is used to calculate relevant shape-related quantities, such as integrated areas, lengths, fit parameters and other detector-specific traits, that are then used by the classification algorithms. This work will follow the latter approach.…”
Section: Pulse Features and Data Preprocessingmentioning
confidence: 99%