dm2gal: Mapping Dark Matter to Galaxies with Neural Networks

TL;DR

This paper introduces dm2gal, a neural network model that predicts galaxy stellar masses from dark matter simulations, enabling fast, accurate galaxy distribution modeling for cosmological studies.

Contribution

The work presents a novel neural network approach that outperforms existing models in mapping galaxy stellar masses onto dark matter fields from gravity-only simulations.

Findings

Model outperforms benchmark in accuracy

Enables rapid galaxy distribution predictions

Improves efficiency of cosmological simulations

Abstract

Maps of cosmic structure produced by galaxy surveys are one of the key tools for answering fundamental questions about the Universe. Accurate theoretical predictions for these quantities are needed to maximize the scientific return of these programs. Simulating the Universe by including gravity and hydrodynamics is one of the most powerful techniques to accomplish this; unfortunately, these simulations are very expensive computationally. Alternatively, gravity-only simulations are cheaper, but do not predict the locations and properties of galaxies in the cosmic web. In this work, we use convolutional neural networks to paint galaxy stellar masses on top of the dark matter field generated by gravity-only simulations. Stellar mass of galaxies are important for galaxy selection in surveys and thus an important quantity that needs to be predicted. Our model outperforms the state-of-the-art benchmark model and allows the generation of fast and accurate models of the observed galaxy distribution.