Cosmic Inflation and Genetic Algorithms

TL;DR

This paper demonstrates that genetic algorithms can efficiently generate and analyze a wide variety of inflationary models consistent with current cosmological constraints, revealing preferences for certain tensor-to-scalar ratios and exploring advanced search methods.

Contribution

It introduces a modular genetic algorithm framework for constructing inflationary models, including polynomial, cosine, and exponential potentials, and compares reinforcement learning with genetic programming for model discovery.

Findings

Approximately 300,000 viable inflation models identified.

Preference for tensor-to-scalar ratio between 0.0001 and 0.0004.

Genetic programming can discover new functional forms for inflationary potentials.

Abstract

Large classes of standard single-field slow-roll inflationary models consistent with the required number of e-folds, the current bounds on the spectral index of scalar perturbations, the tensor-to-scalar ratio, and the scale of inflation can be efficiently constructed using genetic algorithms. The setup is modular and can be easily adapted to include further phenomenological constraints. A semi-comprehensive search for sextic polynomial potentials results in roughly O(300,000) viable models for inflation. The analysis of this dataset reveals a preference for models with a tensor-to-scalar ratio in the range 0.0001 < r < 0.0004. We also consider potentials that involve cosine and exponential terms. In the last part we explore more complex methods of search relying on reinforcement learning and genetic programming. While reinforcement learning proves more difficult to use in this context, the genetic programming approach has the potential to uncover a multitude of viable inflationary models with new functional forms.