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Noise signal identification in time projection chamber data using deep learning model

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Title
Noise signal identification in time projection chamber data using deep learning model
Author(s)
Kim, C.H.; Sunghoon Ahn; Chae, K.Y.; Hooker, J.; Rogachev, G.V.
Publication Date
2023-03
Journal
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, v.1048
Publisher
Elsevier B.V.
Abstract
Deep learning has been employed in various scientific fields and has provided promising results. In this study, a deep learning classifier was implemented to improve the quality of data obtained from a time projection chamber. Digital waveforms of the detected signals were classified into the following three categories: particles, noises, and particles piled up with noises. A simple 1-dimensional convolutional neural network was developed for the classification. The model demonstrated an excellent performance on the test dataset. Its practical performance was also examined using track images and particle identification plots by comparing the original and clean data without the noise signals. The comparison clearly showed that the deep learning model improved the quality of data. The current study presents an effective application of the deep learning model for the time projection chamber data. © 2023 Elsevier B.V.
URI
https://pr.ibs.re.kr/handle/8788114/13390
DOI
10.1016/j.nima.2023.168025
ISSN
0168-9002
Appears in Collections:
Center for Exotic Nuclear Studies(희귀 핵 연구단) > 1. Journal Papers (저널논문)
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