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Restoring original signals from pile-up using deep learning

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Title
Restoring original signals from pile-up using deep learning
Author(s)
Kim, C.H.; Sunghoon Ahn; Chae, K.Y.; Hooker, J.; Rogachev, G.V.
Publication Date
2023-10
Journal
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, v.1055
Publisher
Elsevier BV
Abstract
Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainties and cause multiple problems. Therefore, the correction of pile-up signals is crucially required. In this study, we implemented a deep learning method to restore the original signals from signals piled up with unwanted signals. We showed that a deep learning model could accurately reconstruct the original signal waveforms from the pile-up waveforms. By substituting the pile-up signals with the original signals predicted by the model, the energy and timing resolution of the data are notably enhanced. The model implementation significantly improved the quality of the particle identification plot and particle tracks. This method is applicable to similar problems, such as separating multiple signals or correcting pile-up signals with other types of noises and backgrounds. © 2023 Elsevier B.V.
URI
https://pr.ibs.re.kr/handle/8788114/14526
DOI
10.1016/j.nima.2023.168492
ISSN
0168-9002
Appears in Collections:
Center for Exotic Nuclear Studies(희귀 핵 연구단) > 1. Journal Papers (저널논문)
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