Galaxy Image Simulation Using Progressive GANs

Understand

In this work, we provide an efficient and realistic data-driven approach to simulate astronomical images using deep generative models from machine learning.

• Our solution is based on a variant of the generative adversarial network (GAN) with progressive training methodology and Wasserstein cost function.
• The proposed solution generates naturalistic images of galaxies that show complex structures and high diversity, which suggests that data-driven simulations using machine learning can replace many of the expensive model-driven methods used in astronomical data processing.