2014/12/10 by J. Prasad, Jayanti Prasad, Prasad, Jayanti
Physics and Astronomy · #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Radio Astronomy Observations and Technology #Scientific Research and Discoveries #astro-ph.CO
paper · pdf · doi:10.48550/arxiv.1412.3298
14 Pages, 6 figures and 5 tables
arxiv created 2014/12/10 · openalex publication_date 2014/12/10 · arxiv updated 2014/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Constraining theoretical models with measuring the parameters of those from cosmic microwave background (CMB) anisotropy data is one of the most active areas in cosmology. WMAP, Planck and other recent experiments have shown that the six parameters standard ΛCDM cosmological model still best fits the data. Bayesian methods based on Markov-Chain Monte Carlo (MCMC) sampling have been playing leading role in parameter estimation from CMB data. In one of the recent studies \cite2012PhRvD..85l3008P we have shown that particle swarm optimization (PSO) which is a population based search procedure can also be effectively used to find the cosmological parameters which are best fit to the WMAP seven year data. In the present work we show that PSO not only can find the best-fit point, it can also sample the parameter space quite effectively, to the extent that we can use the same analysis pipeline to process PSO sampled points which is used to process the points sampled by Markov Chains, and get consistent results. We also present implementations of downhill-simplex Method of Nelder and Mead and Powell's method of Bound Optimization BY Quadratic Approximation (BOBYQA) in this work for cosmological parameter estimation, and compare these methods with PSO. Since PSO has the advantage that it only needs the search range and does not need covariance-matrix, starting point or any other quantity which depend on the final results, it can be quite useful for a blind search of the best fit parameters. Apart from that, PSO is based on a completely different algorithm so it can supplement MCMC methods. We use PSO to estimate parameters from the WMAP nine year and Planck data and get consistent results.