# The VIMOS Public Extragalactic Redshift Survey (VIPERS). Measuring nonlinear galaxy bias at z~0.8

- Authors
- Type
- Preprint
- Publication Date
- Jul 07, 2016
- Submission Date
- Jun 25, 2014
- Identifiers
- arXiv ID: 1406.6692
- Source
- arXiv
- License
- Yellow
- External links

## Abstract

We use the first release of the VImos Public Extragalactic Redshift Survey of galaxies (VIPERS) of ~50,000 objects to measure the biasing relation between galaxies and mass in the redshift range z=[0.5,1.1]. We estimate the 1-point distribution function [PDF] of VIPERS galaxies from counts in cells and, assuming a model for the mass PDF, we infer their mean bias relation. The reconstruction of the bias relation from PDFs is performed through a novel method that accounts for Poisson noise, redshift distortions, inhomogeneous sky coverage and other selection effects. With this procedure we constrain galaxy bias and its deviations from linearity down to scales as small as 4 Mpc/h and out to z=1.1. We detect small (~3%) but significant deviations from linear bias. The mean biasing function is close to linear in regions above the mean density. The mean slope of the biasing relation is a proxy to the linear bias parameter. It increases both with luminosity, in agreement with results of previous analyses, and with redshift. However, we detect a strong bias evolution only for z>0.9 in agreement with some, but not all, previous studies. We also detected a significant increase of the bias with the scale, from 4 to 8 Mpc/h, now seen for the first time out to z=1. The amplitude of nonlinearity depends on redshift, luminosity and on scales but no clear trend is detected. Thanks to the large cosmic volume probed by VIPERS we find that the mismatch between the previous estimates of bias at z~1 from zCOSMOS and VVDS-Deep galaxy samples is fully accounted for by cosmic variance. The results of our work confirm the importance of going beyond the over-simplistic linear bias hypothesis showing that non-linearities can be accurately measured through the applications of the appropriate statistical tools to existing datasets like VIPERS.