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

Understand

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.