Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory
| Autor: | Christopher Hartwig Schwarzlmüller, Andreas Windisch, Thomas Gallien |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Toy model Computer science Plane (geometry) Analytic continuation Mathematical analysis Propagator FOS: Physical sciences Computational Physics (physics.comp-ph) 01 natural sciences Loop integral Square (algebra) 010305 fluids & plasmas Machine Learning (cs.LG) High Energy Physics - Phenomenology High Energy Physics - Phenomenology (hep-ph) 0103 physical sciences Reinforcement learning 010306 general physics Complex plane Physics - Computational Physics |
| Popis: | In this paper we present a novel technique based on deep reinforcement learning that allows for numerical analytic continuation of integrals that are often encountered in one-loop diagrams in quantum field theory. In order to extract certain quantities of two-point functions, such as spectral densities, mass poles or multi-particle thresholds, it is necessary to perform an analytic continuation of the correlator in question. At one-loop level in Euclidean space, this results in the necessity to deform the integration contour of the loop integral in the complex plane of the square of the loop momentum, in order to avoid non-analyticities in the integration plane. Using a toy model for which an exact solution is known, we train a reinforcement learning agent to perform the required contour deformations. Our study shows great promise for an agent to be deployed in iterative numerical approaches used to compute non-perturbative 2-point functions, such as the quark propagator Dyson-Schwinger equation, or more generally, Fredholm equations of the second kind, in the complex domain. |
| Databáze: | OpenAIRE |
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