Baryon density extraction and isotropy analysis of Cosmic Microwave
  Background using Deep Learning
  The discovery of cosmic microwave background (CMB) was a paradigm shift in
the study and fundamental understanding of the early universe and also the Big
Bang phenomenon. Cosmic microwave background is one of the richest and
intriguing sources of information available to cosmologists and one parameter
of special interest is baryon density of the universe. Baryon density can be
primarily estimated by analyzing CMB data or through the study of big bang
nucleosynthesis(BBN). Hence, it is necessary that both of the results found
though the two methods are in agreement with each other. Although there are
some well established statistical methods for the analysis of CMB to estimate
baryon density, here we explore the use of deep learning in this respect. We
correlate the baryon density obtained from the power spectrum of simulated CMB
temperature maps with the corresponding map image and form the dataset for
training the neural network model. We analyze the accuracy with which the model
is able to predict the results from a relatively abstract dataset considering
the fact that CMB is a Gaussian random field. CMB is anisotropic due to
temperature fluctuations at small scales but on a larger scale CMB is
considered isotropic, here we analyze the isotropy of CMB by training the model
with CMB maps centered at different galactic coordinates and compare the
predictions of neural network models.
