{"ID":2891098,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18824","arxiv_id":"2507.18824","title":"Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification","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)).","short_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 minimizati...","url_abs":"https://arxiv.org/abs/2507.18824","url_pdf":"https://arxiv.org/pdf/2507.18824v1","authors":"[\"Daniel Sadasivan\",\"Isaac Cordero\",\"Andrew Graham\",\"Cecilia Marsh\",\"Daniel Kupcho\",\"Melana Mourad\",\"Maxim Mai\"]","published":"2025-07-24T21:49:58Z","proceeding":"hep-ph","tasks":"[\"hep-ph\",\"nucl-th\",\"stat.AP\",\"stat.ML\"]","methods":"[]","has_code":false}