Abstract: Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical parameters can vary significantly with the chosen physics model, highlighting the importance of properly accounting for theoretical uncertainties. In this work, we explicitly incorporate these uncertainties using Gaussian processes that model the domain of validity of theoretical models, integrating prior knowledge about where a theory applies and where it does not. We demonstrate the effectiveness of this approach using two systems: a simple ball drop experiment and multi-stage heavy-ion simulations. In both cases incorporating model discrepancy leads to improved parameter estimates, with systematic improvements observed as additional experimental observables are integrated. (arXiv: 2504.13144)

About the Speaker: Dr. Sunil Jaiswal a Postdoctoral Research Associate at Ohio State University. He did his PhD at TIFR Mumbai, where he worked with Prof. Subrata Pal on theoretical formulations of relativistic hydrodynamics and its applications to heavy-ion collisions. At Ohio State University, he work with the BAND collaboration towards developing open-source statistical tools to quantify experimental and theoretical uncertainties for robust model-to-data comparison. Dr. Jaiswal is interested in taking a data driven approach using these tools to understand the properties of the nuclear matter formed in heavy-ion collisions, like shear and bulk viscosities of QGP and its equation of state.

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