Grid-based exploration of cosmological parameter space with Snake

TL;DR

This paper introduces Snake, a parallelized grid-based algorithm for cosmological parameter estimation that efficiently explores high-dimensional likelihoods, providing advantages over traditional MCMC methods.

Contribution

The paper presents Snake, a novel grid-based parameter estimation method that scales well with up to 12 parameters and offers improved sampling and evidence calculation capabilities.

Findings

Snake achieves consistent cosmological parameter estimates with WMAP-7 data.

The method scales efficiently up to 12 parameters.

Snake provides shorter wall times due to perfect parallelization.

Abstract

We present a fully parallelized grid-based parameter estimation algorithm for investigating multidimensional likelihoods called Snake, and apply it to cosmological parameter estimation. The basic idea is to map out the likelihood grid-cell by grid-cell according to decreasing likelihood, and stop when a certain threshold has been reached. This approach improves vastly on the "curse of dimensionality" problem plaguing standard grid-based parameter estimation simply by disregarding grid-cells with negligible likelihood. The main advantages of this method compared to standard Metropolis-Hastings MCMC methods include 1) trivial extraction of arbitrary conditional distributions; 2) direct access to Bayesian evidences; 3) better sampling of the tails of the distribution; and 4) nearly perfect parallelization scaling. The main disadvantage is, as in the case of brute-force grid-based evaluation, a dependency on the number of parameters, N_par. One of the main goals of the present paper is to determine how large N_par can be, while still maintaining reasonable computational efficiency; we find that N_par = 12$ is well within the capabilities of the method. The performance of the code is tested by comparing cosmological parameters estimated using Snake and the WMAP-7 data with those obtained using CosmoMC, the current standard code in the field. We find fully consistent results, with similar computational expenses, but shorter wall time due to the perfect parallelization scheme.