标题： 基于深度神经网络的模拟驱动推断方法在模型误指情况下的极位置估计 显示英文标题

标题： Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification

摘要： 基于仿真的推断（SBI）在某些模型不正确的情况下被证明比传统的卡方最小化方法能提供更准确的共振参数估计，这一点通过π-π散射和rho(770)共振的案例研究得到了验证。 使用卡方最小化拟合某些数据集的模型可能对rho(770)的极点位置预测不准确，而SBI在相同模型和数据下提供了更稳健的预测。 这一结果具有重要意义，既作为SBI能够处理模型不正确性的概念验证，也因为对π-π散射的准确建模在许多当代物理系统的研究中至关重要（例如a1(1260)，omega(782)）。 显示英文摘要

摘要： Simulation Based Inference (SBI) is shown to yield more accurate resonance parameter estimates than traditional chi-squared minimization in certain cases of model misspecification, demonstrated through a case study of pi-pi scattering and the rho(770) resonance. Models fit to some data sets using chi-squared minimization can predict inaccurate pole positions for the rho(770), while SBI provides more robust predictions across the same models and data. This result is significant both as a proof of concept that SBI can handle model misspecification, and because accurate modeling of pi-pi scattering is essential in the study of many contemporary physical systems (e.g., a1(1260), omega(782)).