Speaker
Description
Obtaining the electric conductivity for out-of-equilibrium magnetized QCD mainly faces the challenge of a numerically ill-posed problem. This problem arises when extracting the spectral function from noisy Euclidean correlators on a finite lattice. Recently, a number of novel frameworks have been proposed to resolve the ill-posed problem, including, for instance, machine learning techniques. The small frequency behaviour of the spectral function obtained from these methods can be related to the electric conductivity coefficient via a Kubo formula. In this work, we study the electric conductivity coefficient at non-zero external magnetic fields for Wilson fermions in quenched lattice QCD. We account for systematic effects by using machine learning methods as well as other state-of-the-art spectral reconstruction methods to compute the electric conductivity.