GANs for generating EFT models

TL;DR

This paper introduces a novel framework using GANs to generate effective field theory models, specifically supersymmetric theories, producing new models with properties not present in the training data.

Contribution

It is the first to apply GANs for generating consistent effective field theories, demonstrating the ability to produce models with novel features beyond known examples.

Findings

Generated supersymmetric models with new properties.

Produced models with a different number of minima in potentials.

Demonstrated GANs can explore theoretical model space.

Abstract

We initiate a way of generating models by the computer, satisfying both experimental and theoretical constraints. In particular, we present a framework which allows the generation of effective field theories. We use Generative Adversarial Networks to generate these models and we generate examples which go beyond the examples known to the machine. As a starting point, we apply this idea to the generation of supersymmetric field theories. In this case, the machine knows consistent examples of supersymmetric field theories with a single field and generates new examples of such theories. In the generated potentials we find distinct properties, here the number of minima in the scalar potential, with values not found in the training data. We comment on potential further applications of this framework.