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 literature and densely connected models, and demonstrates higher cost-efficiency than conventional machine learning methods.

翻译：基于闪烁体的粒子探测器广泛应用于高能物理与天体粒子物理实验、核医学成像、工业与环境检测等领域。在事件级别精确提取闪烁信号特征对这些应用至关重要，这不仅有助于理解闪烁体本身特性，还能揭示入射粒子的种类与物理性质。近期研究表明，当信号解析形式难以获取或噪声显著时，数据驱动的神经网络方法优于传统统计方法。然而，大多数基于密集连接或卷积的网络未能充分利用闪烁信号的频谱与时间结构，存在较大的性能提升空间。本文基于时间序列分析的前期工作，提出一种专门针对闪烁脉冲表征设计的网络架构。其核心思想是：通过对原始信号直接应用快速傅里叶变换并利用不同频率分量，该网络架构可作为轻量化且增强的表征学习主干。我们通过两个案例验证该思想：(a) 基于LUX暗物质探测器参数生成的模拟数据；(b) 使用快速电子学设备模拟NICA/MPD量能器闪烁变化的实验电信号。所提模型在文献参考模型与密集连接模型对比中取得显著更优结果，并展现出比传统机器学习方法更高的成本效益。