A Method for Measuring the Bias of High-Redshift Galaxies from Cosmic Variance

TL;DR

This paper introduces a method to measure the clustering bias of high-redshift galaxies by analyzing the field-to-field scatter in their number densities, accounting for cosmic variance, and using simulations to estimate halo masses.

Contribution

The paper presents a novel approach to determine galaxy bias at high redshift from cosmic variance, enabling better understanding of galaxy formation in the early universe.

Findings

Method effectively isolates cosmic variance from Poisson noise.

Hubble Space Telescope parallel programs can measure galaxy bias at z>6.

Simulations link measured bias to halo mass estimates.

Abstract

As deeper observations discover increasingly distant galaxies, characterizing the properties of high-redshift galaxy populations will become increasingly challenging and paramount. We present a method for measuring the clustering bias of high-redshift galaxies from the field-to-field scatter in their number densities induced by cosmic variance. Multiple widely-separated fields are observed to provide a large number of statistically-independent samples of the high-redshift galaxy population. The expected Poisson uncertainty is removed from the measured dispersion in the distribution of galaxy number counts observed across these many fields, leaving, on average, only the contribution to the scatter expected from cosmic variance. With knowledge of the Lambda Cold Dark Matter power spectrum, the galaxy bias is then calculated from the measured cosmic variance. The results of cosmological N-body simulations can then be used to estimate the halo mass associated with the measured bias. We use Monte Carlo simulations to demonstrate that Hubble Space Telescope pure parallel programs will be able to determine galaxy bias at z>~6 using this method, complementing future measurements from correlation functions.