TL;DR
This paper provides tools and models to estimate cosmic variance in small-area high-redshift galaxy surveys, aiding in experiment design and data interpretation by quantifying uncertainties due to large-scale structure fluctuations.
Contribution
It introduces a practical recipe and software for calculating cosmic variance based on survey geometry, redshift, and galaxy stellar mass, incorporating galaxy bias predictions.
Findings
Cosmic variance can reach ~38% for massive galaxies in small fields at z=2.
Larger survey fields significantly reduce cosmic variance.
The provided tools enable more accurate uncertainty estimates in high-redshift galaxy studies.
Abstract
Deep pencil beam surveys (<1 deg^2) are of fundamental importance for studying the high-redshift universe. However, inferences about galaxy population properties are in practice limited by 'cosmic variance'. This is the uncertainty in observational estimates of the number density of galaxies arising from the underlying large-scale density fluctuations. This source of uncertainty can be significant, especially for surveys which cover only small areas and for massive high-redshift galaxies. Cosmic variance for a given galaxy population can be determined using predictions from cold dark matter theory and the galaxy bias. In this paper we provide tools for experiment design and interpretation. For a given survey geometry we present the cosmic variance of dark matter as a function of mean redshift z and redshift bin size Dz. Using a halo occupation model to predict galaxy clustering, we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
