Deep Generative Models for Galaxy Image Simulations

TL;DR

This paper introduces a deep generative modeling approach for creating realistic galaxy images, improving upon traditional parametric models, and provides tools for community use in astronomical image simulations.

Contribution

It develops a hybrid deep learning and Bayesian model for galaxy morphology, trained on HST data, and integrates it into the GalSim simulation framework.

Findings

Generated galaxy morphologies are more realistic than parametric models.

Model successfully captures complex galaxy structures and statistics.

Provides a community resource for galaxy image simulation tools.

Abstract

Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a fairly limited amount of available space-based data. In this work, we propose a methodology based on Deep Generative Models to create complex models of galaxy morphologies that may meet the image simulation needs of upcoming surveys. We address the technical challenges associated with learning this morphology model from noisy and PSF-convolved images by building a hybrid Deep Learning/physical Bayesian hierarchical model for observed images, explicitly accounting for the Point Spread Function and noise properties. The generative model is further made conditional on physical galaxy parameters, to allow for sampling new light profiles from specific galaxy populations. We demonstrate our ability to train and sample from such a model on galaxy postage stamps from the HST/ACS COSMOS survey, and validate the quality of the model using a range of second- and higher-order morphology statistics. Using this set of statistics, we demonstrate significantly more realistic morphologies using these deep generative models compared to conventional parametric models. To help make these generative models practical tools for the community, we introduce GalSim-Hub, a community-driven repository of generative models, and a framework for incorporating generative models within the GalSim image simulation software.