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Cosmic Inflation and Genetic Algorithms
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DOI：10.1002/prop.202200161.png)
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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 similar to O(300,000)$\sim \mathcal {O}(300,000)$ 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$0.0001\le r\le 0.0004$. 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.
Keyword:
artificial intelligence
cosmic inflation
genetic algorithms
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