A machine learning-based method for populating dark matter halos in N-body simulations with substructure
astro-ph.CO
Submitted: 2026-09-30
Updated: 2026-09-30
Terminology
Sources
- The DESI Experiment Part I: Science,Targeting, and Survey Design
- Euclid. I. Overview of the Euclid mission
- LSST: from Science Drivers to Reference Design and Anticipated Data Products
- The Connection between Galaxies and their Dark Matter Halos
- A Comprehensive Analysis of Uncertainties Affecting the Stellar Mass - Halo Mass Relation for 0<z<4
- The Connection between Galaxies and Dark Matter Structures in the Local Universe
- Modeling color-dependent galaxy clustering in cosmological simulations
- The Dark Side of Galaxy Color
- Modeling Redshift-Space Clustering with Abundance Matching
- Consistent and simultaneous modelling of galaxy clustering and galaxy-galaxy lensing with Subhalo Abundance Matching
- Constraints on the relationship between stellar mass and halo mass at low and high redshift
- How do galaxies populate Dark Matter halos?
- Modelling galaxy stellar mass evolution from z~0.8 to today
- Connecting Galaxies, Halos, and Star Formation Rates Across Cosmic Time
- Galactic star formation and accretion histories from matching galaxies to dark matter haloes
- The Average Star Formation Histories of Galaxies in Dark Matter Halos from z=0-8
- Connecting massive galaxies to dark matter halos in BOSS - I. Is galaxy color a stochastic process in high-mass halos?
- The Concentration Dependence of the Galaxy-Halo Connection: Modeling Assembly Bias with Abundance Matching
- Rosella: A mock catalogue from the P-Millennium simulation
- The MillenniumTNG Project: Inferring cosmology from galaxy clustering with accelerated N-body scaling and subhalo abundance matching
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