Efficient representation learning of scintillation signal characteristics with spectrum-inspired temporal neural networks

• URL: http://arxiv.org/abs/2410.07267v1
• Date: Wed, 9 Oct 2024 02:44:53 GMT
• Title: Efficient representation learning of scintillation signal characteristics with spectrum-inspired temporal neural networks
• Authors: Pengcheng Ai, Xiangming Sun, Zhi Deng, Xinchi Ran,
• Abstract summary: Nuclear radiation detectors based on scintillators are widely used in particle and high energy physics experiments, nuclear medicine imaging, industrial and environmental detection, etc. We propose a network architecture specially tailored for scintillation signal characterization based on previous works on time series analysis.
• Score: 1.124958340749622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nuclear radiation detectors based on scintillators are widely used in particle and high energy 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 are superior to 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 signal characterization based on previous works on time series analysis. By directly applying Fast Fourier Transform on original signals without data embedding, including the zero-frequency component, adjusting convolution scheme for low-frequency components, and unbiasedly re-weighting features from different frequencies, the proposed network architecture can serve as a lightweight and enhanced representation learning backbone. We prove our idea on simulation data generated with the setting of the LUX dark matter detector, and on experimental electrical signals with fast electronics to emulate scintillation variations. The proposed model achieves significantly better results than the reference model in literature and densely connected models without representation learning.

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