The Redshift-Space Cluster-Galaxy Cross-Correlation Function: I. Modeling Galaxy Infall onto Millennium Simulation Clusters and SDSS Groups
Ying Zu (OSU), David H. Weinberg (OSU)

TL;DR
This paper develops a method to extract galaxy infall kinematics from redshift-space cross-correlation data, enabling insights into cluster mass profiles and tests of gravity theories using simulations and SDSS data.
Contribution
The paper introduces an analytic model for galaxy infall kinematics and demonstrates its effectiveness in recovering velocity profiles from observational data.
Findings
Accurately predicts redshift-space correlation functions from real-space models.
Recovers galaxy infall velocity profiles consistent with simulations.
Provides a new diagnostic for testing gravity theories with galaxy clustering data.
Abstract
The large scale infall of galaxies around massive clusters provides a potentially powerful diagnostic of structure growth, dark energy, and cosmological deviations from General Relativity. We develop and test a method to recover galaxy infall kinematics (GIK) from measurements of the redshift-space cluster-galaxy cross-correlation function \xi_{cg}(r_p,r_\pi). Using galaxy and halo samples from the Millennium simulation, we calibrate an analytic model of the galaxy kinematic profiles comprised of a virialized component with an isotropic Gaussian velocity distribution and an infall component described by a skewed 2D t-distribution with a characteristic infall velocity v_r and separate radial and tangential dispersions. We show that convolving the real-space cross-correlation function with this velocity distribution accurately predicts the redshift-space \xi_{cg}, and we show that…
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