  Dyson-Schwinger equations (DSEs) are a non-perturbative way to express
n-point functions in quantum field theory. Working in Euclidean space and in
Landau gauge, for example, one can study the quark propagator Dyson-Schwinger
equation in the real and complex domain, given that a suitable and tractable
truncation has been found. When aiming for solving these equations in the
complex domain, that is, for complex external momenta, one has to deform the
integration contour of the radial component in the complex plane of the loop
momentum expressed in hyper-spherical coordinates. This has to be done in order
to avoid poles and branch cuts in the integrand of the self-energy loop. Since
the nature of Dyson-Schwinger equations is such, that they have to be solved in
a self-consistent way, one cannot analyze the analytic properties of the
integrand after every iteration step, as this would not be feasible. In these
proceedings, we suggest a machine learning pipeline based on deep learning (DL)
approaches to computer vision (CV), as well as deep reinforcement learning
(DRL), that could solve this problem autonomously by detecting poles and branch
cuts in the numerical integrand after every iteration step and by suggesting
suitable integration contour deformations that avoid these obstructions. We
sketch out a proof of principle for both of these tasks, that is, the pole and
branch cut detection, as well as the contour deformation.
