A machine learning pipeline for autonomous numerical analytic continuation of Dyson-Schwinger equations

• URL: http://arxiv.org/abs/2112.13011v1
• Date: Fri, 24 Dec 2021 09:56:42 GMT
• Title: A machine learning pipeline for autonomous numerical analytic continuation of Dyson-Schwinger equations
• Authors: Andreas Windisch, Thomas Gallien, Christopher Schwarzlmueller
• Abstract summary: Dyson-Schwinger equations (DSEs) are a non-perturbative way to express n-point functions in quantum field theory. One has to deform the integration contour of the radial component in the complex plane of the loop momentum expressed in hyper-spherical coordinates. 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 step.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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