CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning

Cosmic Microwave Background (CMB) has been a cornerstone in many cosmology experiments and studies since it was discovered back in 1964. Traditional computational models like CAMB that are used for generating CMB anisotropy maps are extremely resource intensive and act as a bottleneck in cosmology experiments that require a large amount of CMB data for analysis. In this paper, we present a new approach to the generation of CMB anisotropy maps using a machine learning technique called Generative Adversarial Network (GAN). We train our deep generative model to learn the complex distribution of CMB maps and efficiently generate new sets of CMB data in the form of 2D patches of anisotropy maps. We limit our experiment to the generation of 56{\deg} and 112{\deg} patches of CMB maps. We have also trained a Multilayer perceptron model for estimation of baryon density from a CMB map, we will be using this model for the performance evaluation of our generative model using diagnostic measures like Histogram of pixel intensities, the standard deviation of pixel intensity distribution, Power Spectrum, Cross power spectrum, Correlation matrix of the power spectrum and Peak count.