Predicting halo occupation and galaxy assembly bias with machine learning

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.

21 pages, 17 figures, submitted to MNRAS

References in corpus (12)

• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• The hierarchical formation of the brightest cluster galaxies
• Assembly bias in the clustering of dark matter haloes
• Halo assembly bias and its effects on galaxy clustering
• Photometric redshift analysis in the Dark Energy Survey Science Verification data
• Building disc structure and galaxy properties through angular momentum: The DARK SAGE semi-analytic model
• Spatial Clustering of Dark Matter Halos: Secondary Bias, Neighbor Bias, and the Influence of Massive Neighbors on Halo Properties
• Environmental Effects on Real-Space and Redshift-Space Galaxy Clustering
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys
• Using member galaxy luminosities as halo mass proxies of galaxy groups
• Modeling assembly bias with machine learning and symbolic regression