Kaustav MitraYale University, Frank C. van den BoschYale University, Johannes U. LangeAmerican UniversityDept. of Physics at U. MichiganLeinweber Center at U. Michigan
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引用次数: 0
Abstract
Basilisk is a novel Bayesian hierarchical method for inferring the
galaxy-halo connection, including its scatter, using the kinematics of
satellite galaxies extracted from a redshift survey. In this paper, we
introduce crucial improvements, such as updated central and satellite
selection, advanced modelling of impurities and interlopers, extending the
kinematic modelling to fourth order by including the kurtosis of the
line-of-sight velocity distribution, and utilizing satellite abundance as
additional constraint. This drastically enhances Basilisk's performance,
resulting in an unbiased recovery of the full conditional luminosity function
(central and satellite) and with unprecedented precision. After validating
Basilisk's performance using realistic mock data, we apply it to the SDSS-DR7
data. The resulting inferences on the galaxy-halo connection are consistent
with, but significantly tighter than, previous constraints from galaxy group
catalogues, galaxy clustering and galaxy-galaxy lensing. Using full projected
phase-space information, Basilisk breaks the mass-anisotropy degeneracy, thus
providing precise global constraint on the average orbital velocity anisotropy
of satellite galaxies across a wide range of halo masses. Satellite orbits are
found to be mildly radially anisotropic, in good agreement with the mean
anisotropy for subhaloes in dark matter-only simulations. Thus, we establish
Basilisk as a powerful tool that is not only more constraining than other
methods on similar volumes of data, but crucially, is also insensitive to halo
assembly bias which plagues the commonly used techniques like galaxy clustering
and galaxy-galaxy lensing.