AI 中文总结
该研究提出基于S矩阵引导的神经网络（SINNs）的色散提取方法，确定ππ散射的σ/f₀(500)等共振极点，结果稳健且可适配其他新物理相关反应分析。
AI 中文摘要
我们首次从解析延拓的神经网络中实现了共振极点的色散提取。采用S矩阵引导的神经网络（SINNs）进行训练，该网络满足幺正性、解析性和交叉对称性，无需固定特定振幅参数化。SINN框架可控制表示依赖性、实现受约束的数据选择并强制执行第一性原理。在ππ散射数据上训练的大量网络集合，将相关不确定性传播到所有导出的观测量中。我们对ππ散射的σ/f₀(500)、ρ(770)和f₀(980)极点进行了稳健确定，在确定振幅的同时得到了散射长度，而 Adler 零点作为解析结构的预测出现。结果对网络架构的变化具有稳定性，且该方法可轻松调整以用于分析与新物理搜索相关的其他反应。
英文摘要
We present the first dispersive extraction of resonant poles from analytically continued neural networks. We use S-matrix informed neural networks (SINNs) trained to respect unitarity, analyticity, and crossing symmetry, without fixing a specific amplitude parametrization. The SINN framework controls representation dependence, enables constrained data selection, and enforces first principles. A large ensemble of networks trained on $ππ$ scattering data propagates correlated uncertainties to all derived observables. We obtain robust determinations of the $σ/f_0(500)$, $ρ(770)$, and $f_0(980)$ poles of $ππ$ scattering. Scattering lengths are determined alongside the amplitudes, while Adler zeroes emerge as predictions of the analytic structure. The results are stable against variations of the network architecture, and our approach can easily be adjusted for analysis of other reactions relevant to New Physics searches.
Comments9 pages, 4 figures
