Bayesian Cosmological inference beyond statistical isotropy

27th IUPAP Conference on Computational Physics

物理学;计算机科学

Souradeep, Tarun^1 ; Das, Santanu^1 ; Wandelt, Benjamin^2

IUCAA, Post Bag 4 Ganeshkhind, Pune

411007, India^1

IAP, Lagrange Institute, Sorbonne University, Paris, France^2

关键词: Bayesian inference;    Cosmic microwave backgrounds;    Cosmological parameters;    Covariance structures;    General covariance;    Spherical harmonics;    Statistical isotropy;    Stochastic sampling method;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/759/1/012062/pdf
DOI  :  10.1088/1742-6596/759/1/012062

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

With advent of rich data sets, computationally challenge of inference in cosmology has relied on stochastic sampling method. First, I review the widely used MCMC approach used to infer cosmological parameters and present a adaptive improved implementation SCoPE developed by our group. Next, I present a general method for Bayesian inference of the underlying covariance structure of random fields on a sphere. We employ the Bipolar Spherical Harmonic (BipoSH) representation of general covariance structure on the sphere. We illustrate the efficacy of the method with a principled approach to assess violation of statistical isotropy (SI) in the sky maps of Cosmic Microwave Background (CMB) fluctuations. The general, principled, approach to a Bayesian inference of the covariance structure in a random field on a sphere presented here has huge potential for application to other many aspects of cosmology and astronomy, as well as, more distant areas of research like geosciences and climate modelling.