Constrained functional priors for Bayesian inference of hot QCD matter
Abstract
Bayesian analyses of heavy-ion collisions rely on functional inputs, such as temperature-dependent transport coefficients and the equation of state, whose prior specification remains a significant source of uncertainty. Standard approaches employ low-dimensional parametrizations that can impose artificial correlations and leave the resulting inference sensitive to the assumed functional form. We develop a nonparametric framework for constructing priors over such functions using Gaussian processes, tailored to emulator-based inference in heavy-ion phenomenology. Truncated Karhunen--Loève expansions yield optimized finite-dimensional representations suitable for use as emulator inputs. We investigate how physical constraints can be incorporated into these priors, demonstrating that rejection-based methods produce non-Gaussian measures and induce nontrivial correlations in the expansion coefficients. By contrast, pushforward constructions enforce the constraints while preserving a tractable Gaussian measure in a latent space. These results provide a systematic framework for incorporating physical constraints into functional priors and clarify their effects on the statistical structure of the finite-dimensional representations used in Bayesian inference.
Related
- ROBIN-PIP: Robust Bayesian Field-Level Inference with Physics-Informed Priors
- Bayesian inference of the dense matter equation of state built upon extended Skyrme interactions: A generalization
- Bayesian Constraints on Bosonic Dark Matter in Neutron Stars within a Covariant Density-Functional Equation-of-State Ensemble
- Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors
- Portfolio-Based Constrained Multi-Objective Bayesian Optimization for Materials Design
BibTeX
@misc{guimaraes2026constrained,
title = {{Constrained functional priors for Bayesian inference of hot QCD matter}},
author = {G. Guimarães and L. Perin and M. Luzum},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28786},
eprint = {2608.28786},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28786},
}
RIS
TY - UNPB
TI - Constrained functional priors for Bayesian inference of hot QCD matter
AU - G. Guimarães
AU - L. Perin
AU - M. Luzum
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28786
UR - https://arxiv.org/abs/2608.28786
AB - Bayesian analyses of heavy-ion collisions rely on functional inputs, such as temperature-dependent transport coefficients and the equation of state, whose prior specification remains a significant source of uncertainty. Standard approaches employ low-dimensional parametrizations that can impose artificial correlations and leave the resulting inference sensitive to the assumed functional form. We develop a nonparametric framework for constructing priors over such functions using Gaussian processes, tailored to emulator-based inference in heavy-ion phenomenology. Truncated Karhunen--Loève expansions yield optimized finite-dimensional representations suitable for use as emulator inputs. We investigate how physical constraints can be incorporated into these priors, demonstrating that rejection-based methods produce non-Gaussian measures and induce nontrivial correlations in the expansion coefficients. By contrast, pushforward constructions enforce the constraints while preserving a tractable Gaussian measure in a latent space. These results provide a systematic framework for incorporating physical constraints into functional priors and clarify their effects on the statistical structure of the finite-dimensional representations used in Bayesian inference.
ER -