From lattice QCD to in-medium heavy-quark interactions via deep learning

¹ Department of Physics, McGill University, Montreal, Quebec H3A 2T8, Canada
² Frankfurt Institute for Advanced Studies, Ruth Moufang Strasse 1, D-60438, Frankfurt am Main, Germany
³ Department of Physics, Tsinghua University, Beijing 100084, China
⁴ Physics Department, Brookhaven National Laboratory, Upton, New York 11973, USA

^* e-mail: shuzhe.shi@mcgill.ca

Published online: 1 February 2022

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, which can be characterized by real (V_R(T, r)) and imaginary (V_I(T, r)) potentials, as functions of temperature and spatial separation. 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 (DNN), here, we obtain model-independent results for V_R(T, r) and V_I(T, r). The temperature dependence of V_R(T, r) was found to be very mild between T ≈ 0 − 330 MeV. Meanwhile, V_I(T, r) shows rapid increase with T and r, which is much larger than the perturbation theory based expectations.