The Spatial Distribution of Satellite Galaxies Within Halos: Measuring the Very Small Scale Angular Clustering of SDSS Galaxies
ApJ 806 :125 (2015) We measure the angular clustering of galaxies from the Sloan Digital Sky Survey Data Release 7 in order to probe the spatial distribution of satellite galaxies within their dark matter halos. Specifically, we measure the angular correlation function on very small scales (7-320&qu...
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Zusammenfassung: | ApJ 806 :125 (2015) We measure the angular clustering of galaxies from the Sloan Digital Sky
Survey Data Release 7 in order to probe the spatial distribution of satellite
galaxies within their dark matter halos. Specifically, we measure the angular
correlation function on very small scales (7-320") in a range of luminosity
threshold samples (absolute r-band magnitudes of -18 up to -21) that are
constructed from the subset of SDSS that has been spectroscopically observed
more than once (the so-called plate overlap region). We choose to measure
angular clustering in this reduced survey footprint in order to minimize the
effects of fiber collision incompleteness, which are otherwise substantial on
these small scales. We model our clustering measurements using a fully
numerical halo model that populates dark matter halos in N-body simulations to
create realistic mock galaxy catalogs. The model has free parameters that
specify both the number and spatial distribution of galaxies within their host
halos. We adopt a flexible density profile for the spatial distribution of
satellite galaxies that is similar to the dark matter Navarro-Frenk-White (NFW)
profile, except that the inner slope is allowed to vary. We find that the
angular clustering of our most luminous samples (Mr< -20 and -21) suggests that
luminous satellite galaxies have substantially steeper inner density profiles
than NFW. Lower luminosity samples are less constraining, however, and are
consistent with satellite galaxies having shallow density profiles. Our results
confirm the findings of Watson et al. 2012 while using different clustering
measurements and modeling methodology. |
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DOI: | 10.48550/arxiv.1407.6740 |