NIHAO-UHD: The properties of MW-like stellar disks in high resolution cosmological simulations
Simulating thin and extended galactic disks has long been a challenge in computational astrophysics. We introduce the NIHAO-UHD suite of cosmological hydrodynamical simulations of Milky Way mass galaxies and study stellar disk properties such as stellar mass, size and rotation velocity which agree well with observations of the Milky Way and local galaxies. In particular, the simulations reproduce the age-velocity dispersion relation and a multi-component stellar disk as observed for the Milky Way. Half of our galaxies show a double exponential vertical profile, while the others are well described by a single exponential model which we link to the disk merger history. In all cases, mono-age populations follow a single exponential whose scale height varies monotonically with stellar age and radius. The scale length decreases with stellar age while the scale height increases. The general structure of the stellar disks is already set at time of birth as a result of the inside-out and upside-down formation. Subsequent evolution modifies this structure by increasing both the scale length and height of all mono-age populations. Thus, our results put tight constraints on how much dynamical memory stellar disks can retain over cosmological timescales. Our simulations demonstrate that it is possible to form thin galactic disks in cosmological simulations provided there are no significant stellar mergers at low redshifts. Most of the stellar mass is formed in-situ with only a few percent ($\lesssim5\%$) brought in by merging satellites at early times. Redshift zero snapshots and halo catalogues are publicly available.
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Tobias Buck (edit)
Aura Obreja (add twitter)
Andrea V. Macciò (add twitter)
Ivan Minchev (edit)
Aaron A. Dutton (add twitter)
Jeremiah P. Ostriker (add twitter)
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09/15/19 06:06PM
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higgsinocat: NIHAO-UHD: The properties of MW-like stellar disks in high resolution cosmological simulations. (arXiv:1909.05864v1 [https://t.co/qoXZjNJgOA]) relevance:44% https://t.co/OnVyBuDpJu #darkmatter https://t.co/v1Q0YXMTFy
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