期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 507, 期 4, 页码 4879-4899出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab2464
关键词
galaxies: formation; galaxies: haloes; galaxies: statistics; cosmology: theory; dark matter; large-scale structure of Universe
资金
- National Science Foundation [AST-1612085, FJCI2017-33816]
The study shows that using machine learning to predict halo occupations can help recover galaxy clustering and assembly bias more accurately. Internal halo properties are most important for predicting central galaxies, while the environment plays a critical role for satellite galaxies.
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.
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