用于振幅分析的S矩阵引导神经网络
S-matrix informed neural networks for amplitude analysis
- Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
- University of California, Berkeley（加州大学伯克利分校）
- The College of William & Mary（威廉玛丽学院）
- Università degli Studi di Messina（墨西拿大学）
- Thomas Jefferson National Accelerator Facility（托马斯杰斐逊国家加速器设施）
- Old Dominion University（奥尔德多米宁大学）
- Universidad Nacional de Educación a Distancia (UNED)（西班牙国立远程教育大学）
- INFN Sezione di Catania（国家核物理研究所卡塔尼亚分所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对粒子物理中从有限含噪且不一致测量重构散射振幅的问题，提出S矩阵引导神经网络（SINNs），结合数据选择与不确定性量化，成功应用于ππ散射，结果可靠且可迁移。
AI中文摘要：
从有限、含噪声且相互不一致的测量值中重构散射振幅是粒子物理相关众多反应中常见的不适定逆问题。我们引入S矩阵引导神经网络（SINNs），证明其能在尊重第一性原理的同时直接从数据中学习散射振幅。我们进一步开发了一种新颖的数据选择程序，该程序利用受限神经网络集成的响应来识别一组既符合第一性原理又相互兼容的实验。我们将该框架应用于ππ散射，生成可复用的振幅及相关不确定性，且不依赖固定函数形式。我们通过闭合测试和消融实验验证了结果的残余模型依赖性和训练偏差，发现模型架构对结果的影响可忽略不计。我们的工作流程统一了物理约束表征学习、数据选择和不确定性量化，该策略可迁移至其他散射过程及受不一致数据限制的其他约束物理问题。
英文摘要：
Reconstructing scattering amplitudes from finite, noisy, and mutually inconsistent measurements is an ill-posed inverse problem common to many reactions relevant to particle physics. We introduce S-matrix informed neural networks (SINNs), and demonstrate their ability to learn scattering amplitudes directly from data while respecting first principles. We further develop a novel data selection procedure, which uses the response of constrained neural network ensembles to identify a set of experiments compatible with first principles, and with each other. We apply this framework to $ππ$ scattering, producing reusable amplitudes and correlated uncertainties without relying on a fixed functional form. We validate our results against residual model dependencies and training biases through closure tests and ablations. We find negligible impact of model architecture on our results. Our workflow unifies physics-constrained representation learning, data selection, and uncertainty quantification. Our strategy is transferable to other scattering processes, and other constrained physics problems limited by inconsistent data.
