Baryon density extraction and isotropy analysis of Cosmic Microwave Background using a multilayer perceptron

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. Although there are some well established statistical methods for the analysis of CMB, here we explore the use of deep learning in this respect. We correlate the baryon density obtained from the power spectrum of CMB temperature maps with the corresponding map 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.