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Modelling the galaxy–halo connection with machine learning

Delgado, Ana Maria; Wadekar, Digvijay; Hadzhiyska, Boryana; Bose, Sownak; Hernquist, Lars; Ho, Shirley

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Ana Maria Delgado

Digvijay Wadekar

Boryana Hadzhiyska

Lars Hernquist

Shirley Ho


To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and haloes accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the assumption that the galaxy occupation in a halo is a function of only its mass, however, in reality; the occupation can depend on various other parameters including halo concentration, assembly history, environment, and spin. Using the IllustrisTNG hydrodynamical simulation as our target, we show that machine learning tools can be used to capture this high-dimensional dependence and provide more accurate galaxy occupation models. Specifically, we use a random forest regressor to identify which secondary halo parameters best model the galaxy–halo connection and symbolic regression to augment the standard HOD model with simple equations capturing the dependence on those parameters, namely the local environmental overdensity and shear, at the location of a halo. This not only provides insights into the galaxy formation relationship but also, more importantly, improves the clustering statistics of the modelled galaxies significantly. Our approach demonstrates that machine learning tools can help us better understand and model the galaxy–halo connection, and are therefore useful for galaxy formation and cosmology studies from upcoming galaxy surveys.


Delgado, A. M., Wadekar, D., Hadzhiyska, B., Bose, S., Hernquist, L., & Ho, S. (2022). Modelling the galaxy–halo connection with machine learning. Monthly Notices of the Royal Astronomical Society, 515(2), 2733-2746.

Journal Article Type Article
Acceptance Date Jul 5, 2022
Online Publication Date Jul 22, 2022
Publication Date 2022-09
Deposit Date Sep 5, 2022
Publicly Available Date Sep 5, 2022
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Royal Astronomical Society
Peer Reviewed Peer Reviewed
Volume 515
Issue 2
Pages 2733-2746


Published Journal Article (2.4 Mb)

Copyright Statement
This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2022 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

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