Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification
High Energy Physics - Phenomenology
2025-07-28 v1 Nuclear Theory
Applications
Machine Learning
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)).
Cite
@article{arxiv.2507.18824,
title = {Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification},
author = {Daniel Sadasivan and Isaac Cordero and Andrew Graham and Cecilia Marsh and Daniel Kupcho and Melana Mourad and Maxim Mai},
journal= {arXiv preprint arXiv:2507.18824},
year = {2025}
}
Comments
12 pages, 4 figures