Evaluating the galaxy formation histories predicted by a neural network in pure dark matter simulations

TL;DR

This paper demonstrates that a neural network can predict galaxy properties from dark matter simulations with reasonable accuracy, but faces challenges with low-mass haloes and resolution effects, highlighting progress and remaining issues in galaxy-halo modeling.

Contribution

The study introduces a semi-recurrent neural network that predicts galaxy evolution from dark matter simulations, addressing effects of baryon removal and simulation resolution.

Findings

Accurate summary statistics for star formation and metallicity histories.

Significant inaccuracies for low-mass, slowly growing haloes.

Overabundance of red, quenched galaxies in higher mass haloes.

Abstract

We investigate a series of galaxy properties computed using the merger trees and environmental histories from dark matter only cosmological simulations, using a semi-recurrent neural network producing self-consistent predictions of galaxy evolution, and using stochastic improvements to this model based on similarly predicted Fourier Transforms. We apply these methods to the dark matter only runs of the IllustrisTNG simulations to understand the effects of baryon removal, and to the gigaparsec-volume pure dark matter simulation Uchuu, to understand the effects of the lower resolution or alternative metrics for halo properties. We find that the machine learning model recovers accurate summary statistics derived from the predicted star formation and stellar metallicity histories, and correspondent spectroscopy and photometry. However, the inaccuracies of the model's application to dark simulations are substantial for low mass and slowly growing haloes. For these objects, the halo mass accretion rate is exaggerated due to the lack of stellar feedback, yet the formation of the halo can be severely limited by the absence of low mass progenitors in a low resolution simulation. Furthermore, differences in the structure and environment of higher mass haloes results in an overabundance of red, quenched galaxies. These results signify progress towards a machine learning model which builds high fidelity mocks based on a physical interpretation of the galaxy-halo connection, yet they illustrate the need to account for differences in halo properties and the resolution of the simulation.