S-matrix informed neural networks for amplitude analysis

2026-08-24

AI Summary

本文提出S-矩阵信息神经网络解决粒子物理中散射振幅重构问题，直接从数据学习并尊重基本原理，同时开发新数据选择程序。

Abstract

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.

Problem

• scattering amplitudes
• inverse problem
• particle physics
• noisy data
• inconsistent measurements

Innovation

• S-matrix informed neural networks
• data selection procedure
• uncertainty quantification
• physics-constrained learning
• ensemble response