CWRU PAT Coffee Agenda

Tuesdays 10:30 - 11:30 | Fridays 11:30 - 12:30

+1 Measurements of the Hubble Constant: Tensions in Perspective.

lxj154 +1

+1 Predicting halo occupation and galaxy assembly bias with machine learning.

bump   cxt282 +1

0 Scalar Fields Near Compact Objects: Resummation versus UV Completion.

bump   oxg34 +1

0 Cavity optimization for Unruh effect at small accelerations.

bump   oxg34 +1

0 From Amplitudes to Contact Cosmological Correlators.

bump   lxj154 +1

Showing votes from 2021-07-02 12:30 to 2021-07-06 11:30 | Next meeting is Tuesday Apr 8th, 10:30 am.

users

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astro-ph.CO

  • Predicting halo occupation and galaxy assembly bias with machine learning.- [PDF] - [Article]

    Xiaoju Xu, Saurabh Kumar, Idit Zehavi, Sergio Contreras
     

    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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