Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models

TL;DR

This paper introduces a novel graph-based deep generative model to efficiently produce realistic synthetic galaxy catalogs, significantly reducing computational costs for future cosmological surveys.

Contribution

It presents the first application of a graph neural network-based generative model for creating synthetic galaxy catalogs in astrophysics.

Findings

Generated samples match statistical properties of real simulations

Model captures 2D and 3D galaxy orientations

First use of graph generative models in astrophysics

Abstract

The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surveys, to test and validate analysis methods, suffers from a very high computational cost. In particular, generating mock galaxy catalogs at sufficiently large volume and high resolution will soon become computationally unreachable. In this paper, we address this problem with a Deep Generative Model to create robust mock galaxy catalogs that may be used to test and develop the analysis pipelines of future weak lensing surveys. We build our model on a custom built Graph Convolutional Networks, by placing each galaxy on a graph node and then connecting the graphs within each gravitationally bound system. We train our model on a cosmological simulation with realistic galaxy populations to capture the 2D and 3D orientations of galaxies. The samples from the model exhibit comparable statistical properties to those in the simulations. To the best of our knowledge, this is the first instance of a generative model on graphs in an astrophysical/cosmological context.