Predicting halo occupation and galaxy assembly bias with machine learning

TL;DR

This paper demonstrates that machine learning can accurately predict galaxy occupation and assembly bias from halo properties, enabling realistic mock galaxy catalogues for cosmological studies.

Contribution

The study introduces a machine learning approach to predict galaxy occupation and assembly bias, improving modeling of galaxy-halo connections beyond traditional methods.

Findings

Machine learning accurately predicts galaxy clustering and assembly bias.

Internal halo properties are key for central galaxy predictions.

Environment influences satellite galaxy predictions.

Abstract

Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the halo occupations and recover galaxy clustering and assembly bias in a semi-analytic galaxy formation model. For stellar-mass selected samples, we train a Random Forest algorithm on the number of central and satellite galaxies in each dark matter halo. With the predicted occupations, we create mock galaxy catalogues and measure the clustering and assembly bias. Using a range of halo and environment properties, we find that the machine learning predictions of the occupancy variations with secondary properties, galaxy clustering and assembly bias are all in excellent agreement with those of our target galaxy formation model. Internal halo properties are most important for the central galaxies prediction, while environment plays a critical role for the satellites. Our machine learning models are all provided in a usable format. We demonstrate that machine learning is a powerful tool for modelling the galaxy-halo connection, and can be used to create realistic mock galaxy catalogues which accurately recover the expected occupancy variations, galaxy clustering and galaxy assembly bias, imperative for cosmological analyses of upcoming surveys.