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.

Time series from real data measurements are often noisy, under-sampled, irregularly sampled, and inconsistent across long-term measurements. Typically, in analyzing these time series, particularly within astronomy, it is common to use estimators such as sample entropy and multi-scale entropy that require interpolation to avoid irregular sampling. In this work, we analyze and consider a new entropy estimator that combines permutations, Fourier Coefficients, and Wasserstein distances to address the concern of irregularly sampled data.