Modeling Redshift-Space Clustering with Abundance Matching

Understand

We explore the degrees of freedom required to jointly fit projected and redshift-space clustering of galaxies selected in three bins of stellar mass from the Sloan Digital Sky Survey Main Galaxy Sample (SDSS MGS) using a subhalo abundance matching (SHAM) model.

• We employ emulators for relevant clustering statistics in order to facilitate our analysis, leading to large speed gains with minimal loss of accuracy.
• We are able to simultaneously fit the projected and redshift-space clustering of the two most massive galaxy samples that we consider with just two free parameters: scatter in stellar mass at fixed SHAM proxy and the dependence of the SHAM proxy on dark matter halo concentration.
• We find some evidence for models that include velocity bias, but including orphan galaxies improves our fits to the lower mass samples significantly.