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

D Sadasivan, I Cordero, A Graham, C Marsh… - arXiv preprint arXiv …, 2025 - arxiv.org
Physics paper hep-ph Suggest

… We have presented a method of Simulation Based Inference and shown it to be a viable alternative method to the χ2 minimization in case of model misspecification both …

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BibTeX

@article{2507.18824v1,
Author        = {Daniel Sadasivan and Isaac Cordero and Andrew Graham and Cecilia Marsh and Daniel Kupcho and Melana Mourad and Maxim Mai},
Title         = {Deep Neural Network Driven Simulation Based Inference Method for Pole
  Position Estimation under Model Misspecification},
Eprint        = {2507.18824v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
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)).},
Year          = {2025},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2507.18824v1},
File          = {2507.18824v1.pdf}
}

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