Cosmological parameter estimation using Particle Swarm Optimization

7th International Conference on Gravitation and Cosmology

Prasad, J.^1 ; Souradeep, T.^1

IUCAA, Pune University Campus, Post Bag 4, Ganeshkhind, Pune 411007, India^1

关键词: Cosmic microwave backgrounds;    Cosmological modeling;    Cosmological parameters;    Markov chain Monte Carlo method;    Observational data;    Sampling-based method;    The standard model;    Theoretical modeling;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/484/1/012047/pdf
DOI  :  10.1088/1742-6596/484/1/012047

来源: IOP

【 摘 要 】

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.