Galactification: painting galaxies onto dark matter only simulations using a transformer-based model

TL;DR

This paper introduces a transformer-based model that rapidly generates realistic galaxy catalogs from dark matter simulations, accurately capturing galaxy properties and distributions for cosmological studies.

Contribution

The paper presents the first accelerated forward model using transformers that reproduces detailed galaxy properties and spatial distributions conditioned on dark matter simulations.

Findings

Faithfully reproduces galaxy summary statistics

Captures variation with cosmological parameters

Provides a fast alternative to hydrodynamical simulations

Abstract

Connecting the formation and evolution of galaxies to the large-scale structure is crucial for interpreting cosmological observations. While hydrodynamical simulations accurately model the correlated properties of galaxies, they are computationally prohibitive to run over volumes that match modern surveys. We address this by developing a framework to rapidly generate mock galaxy catalogs conditioned on inexpensive dark-matter-only simulations. We present a multi-modal, transformer-based model that takes 3D dark matter density and velocity fields as input, and outputs a corresponding point cloud of galaxies with their physical properties. We demonstrate that our trained model faithfully reproduces a variety of galaxy summary statistics and correctly captures their variation with changes in the underlying cosmological and astrophysical parameters, making it the first accelerated forward model to capture all the relevant galaxy properties, their full spatial distribution, and their conditional dependencies in hydrosimulations.