Assessing the Viability of Generative Modeling in Simulated Astronomical Observations

Patrick Janulewicz, Laurence Perreault-Levasseur, Tracy Webb

Keywords: astrophysics, inference, galaxy clusters, generative modeling

TL;DR: We use methods for assessing the quality of generative models and apply them to study bias between real and simulated galaxy cluster images.

Abstract: In this paper, we use methods for assessing the quality of generative models and apply them to a problem from the physical sciences. We turn our attention to astrophysics, where cosmological simulations are often used to create mock observations that mimic telescope images. These simulations and their mock observations are often slow and challenging to generate, inspiring some to use generative modeling to enhance the amount of data available to study. In this work, we add realism to simulated images of galaxy clusters and use probability mass estimation to assess their fidelity compared to reality. We find that the simulations show a degree of bias compared to real observations and suggest that researchers applying generative modeling to these systems should proceed with caution.

Submission Number: 107