Cosmological parameter estimation via iterative emulation of likelihoods

TL;DR

This paper introduces an iterative Gaussian emulation method for cosmological parameter estimation that significantly reduces the number of likelihood evaluations needed, enabling faster and more accurate inference with complex models.

Contribution

The authors develop a novel iterative emulation algorithm for likelihood functions, improving efficiency and accommodating stochastic models in cosmological parameter estimation.

Findings

Achieves accurate posterior distributions with 100 times fewer likelihood evaluations than MCMC.

Works effectively across different problem dimensionalities.

Facilitates the use of more sophisticated theoretical models in cosmology.

Abstract

The interpretation of cosmological observables requires the use of increasingly sophisticated theoretical models. Since these models are becoming computationally very expensive and display non-trivial uncertainties, the use of standard Bayesian algorithms for cosmological inferences, such as MCMC, might become inadequate. Here, we propose a new approach to parameter estimation based on an iterative Gaussian emulation of the target likelihood function. This requires a minimal number of likelihood evaluations and naturally accommodates for stochasticity in theoretical models. We apply the algorithm to estimate 9 parameters from the monopole and quadrupole of a mock power spectrum in redshift space. We obtain accurate posterior distribution functions with approximately 100 times fewer likelihood evaluations than an affine invariant MCMC, roughly independently from the dimensionality of the problem. We anticipate that our parameter estimation algorithm will accelerate the adoption of more accurate theoretical models in data analysis, enabling more comprehensive exploitation of cosmological observables.