Cosmic Inflation and Genetic Algorithms
arXiv:2208.13804 · doi:10.1002/prop.202200161
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.
arXiv comment13 pages, 13 figures
References in corpus (14)
- Planck 2018 results. X. Constraints on inflation
- Particle Physics Models of Inflation and the Cosmological Density Perturbation
- Encyclopaedia Inflationaris
- Contemporary Symbolic Regression Methods and their Relative Performance
- Four-year COBE normalization of inflationary cosmologies
- A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms
- A Systematic Approach to Kähler Moduli Stabilisation
- Genetic Algorithms and the Search for Viable String Vacua
- Searching the Landscape of Flux Vacua with Genetic Algorithms
- Genetic Algorithms and Experimental Discrimination of SUSY Models
- Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
- Breeding realistic D-brane models
- The Power of Genetic Algorithms: what remains of the pMSSM?
- Learning to Inflate