https://doi.org/10.1140/epjp/s13360-025-06385-y
Regular Article
Scintillation pulse characterization with spectrum-inspired temporal neural networks: case studies on particle detector signals
1
PLAC, Key Laboratory of Quark and Lepton Physics (MOE), Central China Normal University, No. 152 Luoyu Road, 430079, Wuhan, Hubei, China
2
Hubei Provincial Engineering Research Center of Silicon Pixel Chip & Detection Technology, No. 152 Luoyu Road, 430079, Wuhan, Hubei, China
3
Key Laboratory of Particle and Radiation Imaging (MOE), Department of Engineering Physics, Tsinghua University, No. 30 Shuangqing Road, 100084, Beijing, China
Received:
4
March
2025
Accepted:
29
April
2025
Published online:
22
May
2025
Particle detectors based on scintillators are widely used in high-energy physics and astroparticle physics experiments, nuclear medicine imaging, industrial and environmental detection, etc. Precisely extracting scintillation signal characteristics at the event level is important for these applications, not only in respect of understanding the scintillator itself, but also kinds and physical property of incident particles. Recent researches demonstrate data-driven neural networks surpass traditional statistical methods, especially when the analytical form of signals is hard to obtain, or noise is significant. However, most densely connected or convolution-based networks fail to fully exploit the spectral and temporal structure of scintillation signals, leaving large space for performance improvement. In this paper, we propose a network architecture specially tailored for scintillation pulse characterization based on previous works on time series analysis. The core insight is that, by directly applying fast Fourier transform on original signals and utilizing different frequency components, the proposed network architecture can serve as a lightweight and enhanced representation learning backbone. We prove our idea in two case studies: (a) simulation data generated with the setting of the LUX dark matter detector and (b) experimental electrical signals with fast electronics to emulate scintillation variations for the NICA/MPD calorimeter. The proposed model achieves significantly better results than the reference model in the literature and densely connected models and demonstrates higher cost-efficiency than conventional machine learning methods.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
