Cosmological parameter estimation using Particle Swarm Optimization (PSO)

TL;DR

This paper explores the use of Particle Swarm Optimization (PSO), a stochastic method from engineering, for estimating cosmological parameters from WMAP data, comparing its performance with traditional MCMC techniques.

Contribution

It demonstrates the application of PSO to cosmological parameter estimation and discusses its advantages for extensive searches in high-dimensional spaces.

Findings

PSO yields parameter estimates consistent with MCMC results.

Error bars differ due to different computation methods.

PSO shows promise for efficient high-dimensional searches.

Abstract

Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter space. Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid based approaches) with the dimensionality of the search space. MCMC methods sample the full joint probability distribution (posterior) from which one and two dimensional probability distributions, best fit (average) values of parameters and then error bars can be computed. In the present work we demonstrate the application of another stochastic method, named Particle Swarm Optimization (PSO), that is widely used in the field of engineering and artificial intelligence, for cosmological parameter estimation from WMAP seven years data. We find that there is a good agreement between the values of the best fit parameters obtained from PSO and publicly available code COSMOMC. However, there is a slight disagreement between error bars mainly due to the fact that errors are computed differently in PSO. Apart from presenting the results of our exercise, we also discuss the merits of PSO and explain its usefulness in more extensive search in higher dimensional parameter space.