A Flexible Analytic Model of Cosmic Variance in the First Billion Years

TL;DR

This paper introduces a simple analytic model for cosmic variance at high redshifts, showing it is mainly driven by dark matter halo fluctuations and providing tools to improve galaxy luminosity function estimates.

Contribution

The work presents a novel, flexible analytic model of cosmic variance in the early universe, validated against observations and integrated into a practical Python package.

Findings

Cosmic variance is dominated by dark matter halo fluctuations.

The model matches observed UV luminosity functions at high redshift.

Incorporating priors on cosmic variance improves luminosity function constraints.

Abstract

Cosmic variance is the intrinsic scatter in the number density of galaxies due to fluctuations in the large-scale dark matter density field. In this work, we present a simple analytic model of cosmic variance in the high redshift Universe ($z\sim5-15$). We assume that galaxies grow according to the evolution of the halo mass function, which we allow to vary with large-scale environment. Our model produces a reasonable match to the observed ultraviolet luminosity functions in this era by regulating star formation through stellar feedback and assuming that the UV luminosity function is dominated by recent star formation. We find that cosmic variance in the UVLF is dominated by the variance in the underlying dark matter halo population, and not by differences in halo accretion or the specifics of our stellar feedback model. We also find that cosmic variance dominates over Poisson noise for future high-$z$ surveys except for the brightest sources or at very high redshifts ($z \gtrsim 12$). We provide a linear approximation of cosmic variance for a variety of redshifts, magnitudes, and survey areas through the public Python package galcv. Finally, we introduce a new method for incorporating priors on cosmic variance into estimates of the galaxy luminosity function and demonstrate that it significantly improves constraints on that important observable.