基于sipm的模块化检测器与物理约束深度学习的无标签时序分析

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Science and Technology Pub Date : 2023-10-30 DOI:10.1088/2632-2153/acfd09
Pengcheng Ai, Le Xiao, Zhi Deng, Yi Wang, Xiangming Sun, Guangming Huang, Dong Wang, Yulei Li, Xinchi Ran
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引用次数: 0

摘要

脉冲定时是核仪器中的一个重要课题,从高能物理到辐射成像都有着广泛的应用。虽然高速模数转换器变得越来越发达和易于使用,但其在核探测器信号处理中的潜在用途和优点仍然不确定,部分原因是相关的时序算法尚未完全理解和利用。在本文中,我们提出了一种新的基于深度学习的方法,用于模块化检测器的时序分析,而不需要明确标记事件数据。利用固有的时间相关性,形成一个带有特殊设计的正则化器的无标签损失函数,以监督神经网络(nn)的训练,使其朝着有意义和准确的映射函数方向发展。从数学上证明了该方法所期望的最优函数的存在性,并给出了一个系统的模型训练和标定算法。在以硅光电倍增管为主换能器的两个实验数据集上验证了该方法的有效性。在玩具实验中,神经网络模型实现了8.8 ps的单通道时间分辨率,并在数据集中表现出对概念漂移的鲁棒性。在电磁量热计实验中,对几种神经网络模型(全连接、卷积神经网络和长短期记忆)进行了测试,以证明它们符合潜在的物理约束,并与传统方法进行了比较。总之,无论在理想条件下还是在噪声条件下,该方法都能很好地恢复波形样本的时间信息。
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Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
Abstract 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 this paper, we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labeling 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 (NNs) towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on two experimental datasets based on silicon photomultipliers as main transducers. In the toy experiment, the NN model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. In the electromagnetic calorimeter experiment, several NN models (fully-connected, convolutional neural network and long short-term memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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