Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.
In the paper \"Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning\", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and ...

脉冲定时是核仪器领域的重要研究方向，其应用覆盖高能物理至辐射成像等诸多场景，影响深远。尽管高速模数转换器（high-speed analog-to-digital converters）的技术愈发成熟且普及度不断提升，但它们在核探测器信号处理中的应用潜力与优势仍未明确，部分原因在于相关定时算法尚未得到充分理解与利用。在题为《基于物理约束深度学习的硅光电倍增管（SiPM）模块化探测器无标注定时分析》的论文中，我们提出了一种基于深度学习的新型方法，用于模块化探测器的定时分析，且无需对事件数据进行显式标注。该方法利用固有的时间相关性，构建了带有定制正则项的无标注损失函数，以监督神经网络训练，使其学习到具备物理意义且精度优异的映射关系。我们从数学层面证明了该方法所需最优函数的存在性，且……