Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics

TL;DR

This paper uses deep neural networks to extract temperature-dependent real and imaginary parts of the bottomonium potential in quark-gluon plasma from lattice QCD data, revealing new insights into in-medium quark interactions.

Contribution

It introduces a novel, model-independent neural network approach to determine the in-medium bottomonium potential from lattice QCD results.

Findings

Real potential varies mildly with temperature.

Imaginary potential increases rapidly with temperature and distance.

Results differ significantly from perturbative predictions.

Abstract

Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks. The latter can be characterized by the temperature ($T$) dependent potential, with real ($V_R(T,r)$) and imaginary ($V_I(T,r)$) parts, as a function of the spatial separation ($r$). Recently, the masses and thermal widths of up to $3S$ and $2P$ bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network, here, we obtain $V_R(T,r)$ and $V_I(T,r)$ in a model-independent fashion. The temperature dependence of $V_R(T,r)$ was found to be very mild between $T\approx0-334$~MeV. For $T=151-334$~MeV, $V_I(T,r)$ shows a rapid increase with $T$ and $r$, which is much larger than the perturbation-theory-based expectations.