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

• Published in: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Vol. 1048; no. C; p. 168025
• Main Authors: Kim, C.H., Ahn, S., Chae, K.Y., Hooker, J., Rogachev, G.V.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2023; Elsevier
• Subjects: Deep learning application; Digital waveform; Machine learning application; Micromegas detector; Noise signal; Time projection chamber; Noise signal; Deep learning application; Machine learning application; Digital waveform; Time projection chamber; Micromegas detector
• ISSN: 0168-9002; 1872-9576
• DOI: 10.1016/j.nima.2023.168025

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.