Data-Driven Analysis for the Bottomonium Potential in the Quark-Gluon Plasma

TL;DR

This paper uses a data-driven, Bayesian approach within a quantum framework to extract the bottomonium potential in the quark-gluon plasma, aiding understanding of QGP properties through heavy-ion collision data.

Contribution

It introduces a novel Bayesian method to constrain the bottomonium potential using experimental data and quantum simulations, addressing discrepancies in lattice QCD results.

Findings

Constrained the bottomonium potential parameters using Bayesian analysis.

Provided a testable effective potential for future experiments.

Linked potential parameters to observables via Schrödinger equation simulations.

Abstract

We present a data-driven analysis within a quantum evolutionary microscopic framework to constrain the in-medium bottomonium potential. In relativistic heavy-ion collisions, bottomonium bound states serve as invaluable probes of the quark-gluon plasma (QGP) owing to their negligible production in the QGP phase. Meanwhile, their non-relativistic nature allows a straightforward theoretical description via effective field theories such as potential models. Recent lattice QCD calculations of the bottomonium interaction potential have yielded qualitatively distinct results. These discrepancies motivate a data-driven extraction of the potential based on heavy-ion experiments. In this work, we perform a Bayesian analysis to constrain the bottomonium interaction potential. The relationship between potential parameters and observables is established by numerically solving the non-relativistic time-dependent Schr"odinger equation. By comparing these simulations with experimental measurements, our Bayesian framework provides the effective potential that is readily testable in future experiments.