Scale Dependent Galaxy Bias in the SDSS as a function of Luminosity and Colour

TL;DR

This study investigates how galaxy bias varies with scale, luminosity, and color in SDSS data, revealing that luminous blue galaxies exhibit stronger scale-dependent bias, which is crucial for cosmological analyses.

Contribution

It provides the first detailed analysis of scale-dependent galaxy bias as a function of luminosity and color using SDSS DR5 data, informing future survey designs.

Findings

Luminous blue galaxies show stronger scale-dependent bias.

Red galaxies exhibit weaker luminosity dependence in bias.

Bias transition from scale-independent to scale-dependent varies with galaxy type.

Abstract

It has been known for a long time that the clustering of galaxies changes as a function of galaxy type. This galaxy bias acts as a hindrance to the extraction of cosmological information from the galaxy power spectrum or correlation function. Theoretical arguments show that a change in the amplitude of the clustering between galaxies and mass on large-scales is unavoidable, but cosmological information can be easily extracted from the shape of the power spectrum or correlation function if this bias is independent of scale. Scale-dependent bias is generally small on large scales, k<0.1 h.Mpc^{-1}, but on smaller scales can affect the recovery of \Omega_m.h from the measured shape of the clustering signal, and have a small effect on the Baryon Acoustic Oscillations. In this paper we investigate the transition from scale-independent to scale-dependent galaxy bias as a function of galaxy population. We use the Sloan Digital Sky Survey DR5 sample to fit various models, which attempt to parametrise the turn-off from scale-independent behaviour. For blue galaxies, we find that the strength of the turn-off is strongly dependent on galaxy luminosity, with stronger scale-dependent bias on larger scales for more luminous galaxies. For red galaxies, the scale-dependence is a weaker function of luminosity. Such trends need to be modelled in order to optimally extract the information available in future surveys, and can help with the design of such surveys.