Galaxy Image Simulation Using Progressive GANs

• DOI: 10.48550/arxiv.1909.12160
• Publication Date: 2019-01-01
• Authors: Dia, Mohamad; Savary, Elodie; Melchior, Martin; Courbin, Frederic
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Image and Video Processing (eess.IV); FOS: Physical sciences; Machine Learning (stat.ML); Electrical Engineering and Systems Science - Image and Video Processing; Astrophysics - Astrophysics of Galaxies; 01 natural sciences; Machine Learning (cs.LG); Statistics - Machine Learning; Astrophysics of Galaxies (astro-ph.GA); 0103 physical sciences; FOS: Electrical engineering, electronic engineering, information engineering

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.<br/>Submitted to the Astronomical Data Analysis Software & Systems Conference (ADASS), 2019<br/>