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

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 approximate to 0-334 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.

Automated summary

This article obtains the temperature-dependent potentials for bottomonium states in high-energy nuclear collisions using a novel application of deep neural network. The potentials provide a theoretical basis for experimental studies of bottomonium states in the quark-gluon plasma.