Predicting halo occupation and galaxy assembly bias with machine learning

  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},
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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