Predicting halo occupation and galaxy assembly bias with machine learning

@article{Xu2021PredictingHO,
  title={Predicting halo occupation and galaxy assembly bias with machine learning},
  author={Xiaoju Xu and Saurabh Kumar and Idit Zehavi and Sergio Contreras},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2021}
}
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. Withโ€ฆย Expand
1 Citations
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References

SHOWING 1-10 OF 108 REFERENCES
Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach
We present a machine learning (ML) approach for the prediction of galaxiesโ€™ dark matter halo masses which achieves an improved performance over conventional methods. We train three ML algorithmsโ€ฆ Expand
Halo assembly bias and its effects on galaxy clustering
The clustering of dark haloes depends not only on their mass but also on their assembly history, a dependence we term 'assembly bias'. Using a galaxy formation model grafted on to the Millenniumโ€ฆ Expand
Galaxy assembly bias: a significant source of systematic error in the galaxyโ€“halo relationship
It is common practice for methods that use galaxy clustering to constrain the galaxy-halo relationship, such as the halo occupation distribution (HOD) and/or conditional luminosity function (CLF), toโ€ฆ Expand
Galaxy assembly bias of central galaxies in the Illustris simulation
Galaxy assembly bias, the correlation between galaxy properties and halo properties at fixed halo mass, could be an important ingredient in halo-based modelling of galaxy clustering. We investigateโ€ฆ Expand
The Halo Occupation Distribution: Toward an Empirical Determination of the Relation between Galaxies and Mass
We investigate galaxy bias in the framework of the halo occupation distribution (HOD), which defines the bias of a population of galaxies by the conditional probability P(N|M) that a dark matter haloโ€ฆ Expand
Detection of galaxy assembly bias
Assembly bias describes the finding that the clustering of dark matter haloes depends on halo formation time at fixed halo mass. In this paper, we analyse the influence of assembly bias on galaxyโ€ฆ Expand
The Impact of Assembly Bias on the Galaxy Content of Dark Matter Halos
We study the dependence of the galaxy content of dark matter halos on large-scale environment and halo formation time using semi-analytic galaxy models applied to the Millennium simulation. Weโ€ฆ Expand
Extensions to the halo occupation distribution model for more accurate clustering predictions
We test different implementations of the halo occupation distribution (HOD) model to reconstruct the spatial distribution of galaxies as predicted by a version of the L-GALAXIES semi-analyticalโ€ฆ Expand
The impact of assembly bias on the halo occupation in hydrodynamical simulations.
We investigate the variations in galaxy occupancy of the dark matter haloes with the large-scale environment and halo formation time, using two state-of-the-art hydrodynamical cosmologicalโ€ฆ Expand
The evolution of assembly bias
We examine the evolution of assembly bias using a semi-analytical model of galaxy formation implemented in the Millennium-WMAP7 N-body simulation. We consider fixed number density galaxy samplesโ€ฆ Expand
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