用物理约束神经网络从噪声数据中重建散射幅，无需预设函数形式。
S-matrix informed neural networks for amplitude analysis
- 引入S矩阵约束的神经网络，直接从数据学习散射幅。
- 通过一致性检测选出兼容物理原理的实验数据，提升结果可靠性。
- 适用于ππ散射等复杂过程，结果可复用且带相关不确定性。
从有限、含噪且相互矛盾的测量数据中重构散射幅，是粒子物理中常见的不适定逆问题。本文提出S矩阵约束神经网络（SINNs），在不依赖固定函数形式的前提下，直接从数据学习满足基本物理原理的散射幅。我们进一步开发了一种新型数据选择方法，利用受约束神经网络集合的响应，识别出与基本物理原理及其他实验一致的实验集。该框架应用于ππ散射，生成可复用的散射幅及关联不确定性。通过闭包测试和消融实验验证了结果对模型架构和训练偏差的鲁棒性，影响可忽略。整个流程整合了物理约束表示学习、数据筛选与不确定性量化，策略可推广至其他散射过程及受限于不一致数据的物理问题。
原文摘要 · Abstract (English)
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.
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