Baryonic imprints on DM haloes: population statistics from dwarf galaxies to galaxy clusters

Author:

Anbajagane Dhayaa12ORCID,Evrard August E23ORCID,Farahi Arya45ORCID

Affiliation:

1. Department of Astronomy and Astrophysics, University of Chicago, 5640 S. Ellis Ave, Chicago, IL 60637, USA

2. Department of Physics and Leinweber Center for Theoretical Physics, University of Michigan, Ann Arbor, MI 48109, USA

3. Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA

4. Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA

5. Department of Statistics and Data Science, The University of Texas at Austin, TX 78712, USA

Abstract

ABSTRACT In a purely cold dark matter (CDM) universe, the initial matter power spectrum and its subsequent gravitational growth contain no special mass- or time-scales, and so neither do the emergent population statistics of internal dark matter (DM) halo properties. Using 1.5 million haloes from three illustristng realizations of a ΛCDM universe, we show that galaxy formation physics drives non-monotonic features (‘wiggles’) into DM property statistics across six decades in halo mass, from dwarf galaxies to galaxy clusters. We characterize these features by extracting the halo mass-dependent statistics of five DM halo properties – velocity dispersion, NFW concentration, density- and velocity-space shapes, and formation time – using kernel-localized linear regression (Kllr). Comparing precise estimates of normalizations, slopes, and covariances between realizations with and without galaxy formation, we find systematic deviations across all mass-scales, with maximum deviations of 25 per cent at the Milky Way mass of $10^{12} \, {\rm M}_\odot$. The mass-dependence of the wiggles is set by the interplay between different cooling and feedback mechanisms, and we discuss its observational implications. The property covariances depend strongly on halo mass and physics treatment, but the correlations are mostly robust. Using multivariate Kllr and interpretable machine learning, we show the halo concentration and velocity-space shape are principal contributors, at different mass, to the velocity dispersion variance. Statistics of mass accretion rate and DM surface pressure energy are provided in an appendix. We publicly release halo property catalogues and kllr parameters for the TNG runs at 20 epochs up to z = 12.

Funder

National Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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