Connection between galaxy morphology and dark-matter halo structure II: predicting disk structure from dark-matter halo properties
Jinning Liang (1, 2), Fangzhou Jiang (2), Houjun Mo (3), Andrew Benson (4), Philip F. Hopkins (5), Avishal Dekel (6), Luis C. Ho (2) ((1) DoA Peking University, (2) KIAA Peking University, (3) UMass, (4) Carnegie, (5) Caltech, (6) HUJI)

TL;DR
This study uses simulations to predict galaxy disk sizes and thicknesses from dark-matter halo properties, revealing key parameters and providing empirical relations for galaxy-halo modeling.
Contribution
It introduces machine learning-based methods to accurately predict disk structures from halo properties, highlighting the impact of baryonic effects and offering practical predictive formulas.
Findings
Halo properties can predict disk size and thickness with high accuracy.
Disk height is easier to predict than disk size.
Baryonic restructuring influences inner halo properties and disk-halo correlations.
Abstract
We investigate how galactic disk structures connect to the detailed properties of their host dark-matter halos using the TNG50 simulation. From the hydrodynamic and matched dark-matter-only runs, we measure a comprehensive list of halo properties describing density structure, angular momentum, shape, assembly history, and environment. Using the morphological decomposition developed in Paper I, we quantify the sizes, scale heights, and mass fractions of the disk components for galaxies at . Random Forest (RF) regression shows that halo properties alone predict disk size and thickness with high accuracy, while Symbolic Regression (SR) provides compact empirical relations with slightly lower accuracy. Disk height is consistently easier to predict than disk size, and lower-mass halos yield higher accuracy than massive halos. Predictions based on halo properties measured in…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Dark Matter and Cosmic Phenomena
