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

TL;DR

This paper demonstrates that Simulation Based Inference (SBI) offers more accurate pole position estimates than traditional methods in pi-pi scattering, especially under model misspecification, highlighting its robustness in physical system analysis.

Contribution

The paper introduces a deep neural network driven SBI method that effectively handles model misspecification in resonance parameter estimation, outperforming chi-squared minimization.

Findings

SBI provides more accurate pole estimates than chi-squared minimization.

SBI remains robust under model misspecification.

Application to pi-pi scattering confirms SBI's effectiveness.

Abstract

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)).