Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes

TL;DR

This paper introduces a hybrid model combining an analytic equilibrium formalism with machine learning to efficiently emulate baryonic properties in dark matter haloes, significantly speeding up cosmological simulations.

Contribution

It presents a novel hybrid framework that integrates an analytic galaxy evolution model with machine learning to accurately and rapidly populate dark matter haloes with baryonic properties.

Findings

Outperforms machine learning-only approaches for certain properties.

Provides a fast emulation of hydrodynamic simulations.

Balances accuracy and computational speed effectively.

Abstract

While cosmological dark matter-only simulations relying solely on gravitational effects are comparably fast to compute, baryonic properties in simulated galaxies require complex hydrodynamic simulations that are computationally costly to run. We explore the merging of an extended version of the equilibrium model, an analytic formalism describing the evolution of the stellar, gas, and metal content of galaxies, into a machine learning framework. In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties. While there exists a trade-off between the reached accuracy and the speed advantage this approach offers, our results outperform an approach using only machine learning for a subset of baryonic properties. We demonstrate that this novel hybrid system enables the fast completion of dark matter-only information by mimicking the properties of a full hydrodynamic suite to a reasonable degree, and discuss the advantages and disadvantages of hybrid versus machine learning-only frameworks. In doing so, we offer an acceleration of commonly deployed simulations in cosmology.