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Stellar Property Statistics of Massive Halos: Common Kernel Shapes from Multiple Cosmological Hydrodynamics Simulations

In the last decade, the astrophysical processes driving galaxy formation in a cosmological context at kpc scales have been incorporated, largely independently, into multiple codes developed by different simulation teams. Each simulation solves the complex evolution of baryon components (principally cold/warm/hot gas phases, metals, stars, and supermassive black holes) coupled gravitationally to dark matter, and the realization of large cosmic volumes yields populations of thousands of massive halos that host groups and clusters of galaxies.

This talk will present a recent study of stellar property statistics of massive halo populations realized by three cosmological hydrodynamics simulations: BAHAMAS+MACSIS, TNG300 of the IllustrisTNG suite, and Magneticum Pathfinder. The simulations have spatial resolutions ranging 1.5 to 6 kpc, and each generates samples of 1000 or more halos with total mass >10^{13.5} M⊙ at z = 0. Applying a localized, linear regression (LLR) method, we extract halo mass-conditioned statistics (normalizations, slopes, and intrinsic covariance) for a three-element stellar property vector consisting of: i) Nsat, the number of satellite galaxies with stellar mass >10^{10} M⊙ within radius R200c of the halo; ii) M⋆,tot, the total stellar mass within that radius, and; iii) M⋆,BCG, the gravitationally-bound stellar mass of the central galaxy within a 100 kpc radius. While there is not perfect agreement in scaling relation parameters from the three simulation teams, we find common shapes of normalized property kernels for satellite galaxy count and total stellar mass, and all simulations show an anti-correlation between BCG stellar mass and satellite galaxy count at fixed halo mass, as anticipated from age-related arguments in which the BCGs in early-forming halos grow by accreting satellites to a larger extent than those in late-forming halos. We close with some potential implications and thoughts on how such population studies could be better facilitated through common data analysis and publication practices.

Speaker:  Gus Evrard, Michigan

Tuesday, 01/28/20

Contact:

Website: Click to Visit

Cost:

Free

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