Symbolic Regression Applied to Cosmology: an Approximate Expression for the Density Perturbation Variance

Computations of cosmological properties, such as the density perturbation variance, $\sigma$ , are computationally expensive. In this work we propose the application of Symbolic Regression to generate analytical expressions to approximate these quantities. We created simulated data for $\sigma$ using a Boltzmann solver, CAMB. These simulations cover seven parameters; the five cosmological parameters and the redshift and mass of dark matter halos. We then apply a Symbolic Regression engine, TuringBot, to this simulated data and obtain an analytical equation to approximate $\sigma$ . The resulting mathematical expression has a mean accuracy of ≈ 98.96% over the entire domain, and is five orders of magnitude faster than using simulated data from CAMB, demonstrating the applicability of Symbolic Regression to accelerate cosmological inference.