Bayesian Calibration of the Crossterms Eigenvolume HRG Model: Integrating Lattice QCD and Experimental Data
Nachiketa Sarkar

TL;DR
This study employs Bayesian methods to calibrate a flavor-dependent eigenvolume HRG model using lattice QCD and experimental data, revealing the importance of systematic uncertainties and highlighting limitations in constraining multi-strange hadron parameters.
Contribution
First Bayesian calibration of the Cross EV-HRG model with combined lattice QCD and experimental data, emphasizing the role of systematic uncertainties and data constraints.
Findings
LQCD data alone weakly constrain eigenvolume parameters.
Hadron yield data significantly improve parameter constraints.
Systematic uncertainties critically affect the calibration results.
Abstract
We perform a Bayesian calibration of the Cross--term Excluded-Volume Hadron Resonance Gas (Cross EV--HRG) model, which incorporates flavor-dependent repulsive interactions within a thermodynamically consistent framework. For the first time, the thermal model is simultaneously constrained using lattice QCD (LQCD) thermodynamic observables and centrality-resolved hadron yield data from Pb--Pb collisions at measured by the ALICE Collaboration. We also find that the calibration outcome is strongly data-dependent in terms of constraining power and uncertainty structure. In particular, LQCD observables alone provide only weak constraints on the eigenvolume parameters, while the inclusion of hadron yield data substantially enhances the constraining power and induces a nontrivial reshaping of the posterior distributions. We further investigate the…
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Taxonomy
Topicsdemographic modeling and climate adaptation · Scientific Computing and Data Management · Quantum Chromodynamics and Particle Interactions
