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 temperature 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 temperature maps using a specific class of neural networks 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 without losing much accuracy. We limit our experiment to the generation of 56$^{\circ}$ and 112$^{\circ}$ square 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. We show that the GAN model is able to efficiently generate CMB samples of multiple sizes and is sensitive to the cosmological parameters corresponding to the underlying distribution of the data. The primiary advantage of this method is the exponential reduction in the computational time needed to generate the CMB data, the GAN model is able to generate the samples within seconds as opposed to hours required by the CAMB package with an acceptable value to error and loss of information. We hope that future iterations of this methodology will replace traditional statistical methods of CMB data generation and help in large scale cosmological experiments.