基于物理信息神经网络的高度分割硅望远镜自动校准
Automated Physics-Informed Neural-Networks-Based Calibration of Highly Segmented Silicon Telescopes
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中文总结 AI 辅助
本文提出一种基于神经网络的物理信息自动校准框架，通过全局优化同时确定探测器增益和几何修正，在二体运动学约束下最小化激发能宽度，并在PISTA阵列上验证了其高效性和准确性。
中文摘要 AI 辅助
转移反应和多核子转移反应是探测核结构和反应动力学的重要工具，需要精确确定反应产物的身份、能量和发射角度。现代硅望远镜阵列日益增加的颗粒度增强了实验能力，但给探测器校准带来了挑战，因为传统的逐通道方法变得低效且难以扩展。在本工作中，我们提出了一种基于神经网络的完全自动化的物理信息校准框架，专门设计用于高度分割的硅探测器阵列。该方法将校准表述为一个全局优化问题，其中探测器增益和几何修正通过在二体运动学约束下最小化重建激发能的宽度来同时确定。该方法仅依赖实验数据和公认的物理原理，无需显式建模探测器响应。一个显著特点是使用多个神经网络子模型共享一个带有嵌入物理约束的公共损失函数，从而实现所有探测器通道的连贯且自洽的校准。这一策略确保了可扩展性、鲁棒性和可重复性，使其特别适用于日益复杂的下一代探测器系统。该方法的性能通过粒子鉴别硅望远镜阵列（PISTA）在逆运动学高分辨率裂变研究中的实验数据得到验证。结果表明，该方法与理论运动学高度一致，粒子鉴别质量高，校准效率显著提高。所提出的框架为现代核物理实验中复杂探测器系统的校准提供了一种通用且适应性强的解决方案。
英文摘要
Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determination of the identity, energy, and emission angles of reaction products. The increasing granularity of modern silicon telescope arrays enhances experimental capabilities but challenges detector calibration, as conventional channel-by-channel approaches become inefficient and difficult to scale. In this work, we present a fully automated, physics-informed calibration framework based on neural networks, specifically designed for highly segmented silicon detector arrays. The method formulates calibration as a global optimization problem, in which detector gains and geometrical corrections are determined simultaneously by minimizing the width of the reconstructed excitation energy under two-body kinematics constraints. The approach relies exclusively on experimental data and well-established physical principles, without requiring explicit modeling of detector response. A distinctive feature is the use of multiple neural network sub-models sharing a common loss function with embedded physics constraints, enabling coherent and self-consistent calibration across all detector channels. This strategy ensures scalability, robustness, and reproducibility, making it particularly suitable for next-generation detector systems with increasing complexity. The performance of the method is demonstrated using experimental data from the Particle-Identification Silicon-Telescope Array (PISTA) in high-resolution fission studies in inverse kinematics. The results show excellent agreement with theoretical kinematics, high-quality particle identification, and a significant improvement in calibration efficiency. The proposed framework provides a general and adaptable solution for the calibration of complex detector systems in modern nuclear physics experiments.
发表机构
- GANIL, CEA/DRF - CNRS/IN2P3（大加速器国家实验室，法国原子能和替代能源委员会/核物理与粒子物理研究所 - 法国国家科学研究中心/核物理与粒子物理研究所）
- CEA, DAM, DIF（法国原子能和替代能源委员会，军事应用局，法兰西岛分部）
- Université Paris-Saclay（巴黎萨克雷大学）
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