Cosmic Microwave Background Radiation (CMBR)





Cosmological Parameter Estimation from CMBR data sets

Estimating cosmological parameters from CMBR data is an important exercise in current cosmological research. The aim is to find that which theoretical model fits best to the data, which is most commonly done using Markov-Chain Monte Carlo (MCMC) in which the likelihood (probability of obtaining a data set given a theoretical model) function is sampled at discrete points in the multi-dimensional parameters space. From the MCMC points probability distribution of parameters are obtained to get the best fit value of the parameters. We have demonstrated that an another method called Particle Swarm Optimization or PSO also can be used for this purpose.




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This document was last modified on 02/27/2017 10:10:36