DeepSZSim: Fast Simulations of the Thermal Sunyaev-Zel'dovich Effect in Galaxy Clusters for Simulation-based Inference

Simulations of galaxy clusters that are well-matched to upcoming data sets are a key tool for addressing systematics (e.g., cluster mass inference) that limit current and future cluster-based cosmology constraints. However, most state-of-the-art simulations are too computationally intensive to produce multiple versions of relevant physics systematics. We present DeepSZSim, a lightweight framework for generating simulations of Sunyaev-Zel'dovich (SZ) effect clusters based on average thermal pressure profile models. These simulations offer a fast and flexible method for generating large datasets for testing mass inference methods like machine learning and simulation-based inference. We present these simulations and their place within the larger Deep Skies nexus of versatile, multiwavelength galaxy cluster and cosmic microwave background simulators. We discuss progress and prospects for using these SZ simulations for machine learning, including simulation-based inference of cluster mass.