Abstract Details
| Name: JAYANTI PRASAD Affiliation: IUCAA Conference ID: ASI2015_560 Title : Revisiting cosmological parameter estimation Authors and Co-Authors : JAYANTI PRASAD, Inter-University Centre for Astronomy Astrophysics, Post Box 4, Ganehskhind, Pune - 411007 Abstract Type : Poster Abstract Category : General Relativity and Cosmology Abstract : Cosmic Microwave Background (CMB) anisotropies on the sky are the goldmine of cosmological information like physical densities of the baryons and dark matter, dark energy density, nature and strength of primordial density perturbations which lead to structure formation in the universe etc. In order to get useful information from the CMB experiments like WMAP and Planck we must reduce the huge volume of data these experiments generate first into multi-frequency maps and then into angular power spectrum from which theoretical models can be constrained (CMB anisotropies are considered isotropic and Gaussian so power spectrum has all the information which we have in the CMB sky). In order to estimate the parameters of theoretical models, like $\Lambda$CDM, Bayesian approach is generally followed in which we sample the multi-dimensional parameters space using Markov-Chain Monte Carlo (MCMC) methods and compute the marginalized values of the parameters and error bars on them from the sampled points. In the present work we show that Particle Swarm Optimization (PSO) which is a population based search procedure can not only find the values of the cosmological parameters which give best likelihood for the WMAP and Planck data, it can also sample the parameter space quite effectively, to the extent that we can process the PSO sampled point in the way as we process MCMC sampled point. We also compare PSO with Downhill-Simplex method of Nelder \& Mead and Powell's method of Bound Optimization BY Quadratic Approximation (BOBYQA) in this work. |