Effects of Sampling on Measuring Galaxy Count Probabilities

Abstract

We investigate in detail the effects of sampling on our ability to accurately reconstruct the distribution of galaxies from galaxy surveys. We use a simple probability theory approach, Bayesian classifier theory and Bayesian transition probabilities. We find the best Bayesian estimator for the case of low sampling rates, and show that even in the optimal case certain higher order characteristics of the distribution are irretrievably washed out by sparse sampling: we illustrate this by a simple model for cluster selection. We show that even choosing an optimal threshold, there are nonzero numbers for both misidentified clusters and true clusters missed. The introduction of sampling has an effect on the distribution function that is similar to convolution. Deconvolution is possible and given in the paper, although it might become unstable as sampling rates become low. These findings have important consequences on planning and strategies of future galaxy surveys.

Accepted for publication in ApJ. postscript of 16 pages and three figures uuencoded, gzipped, tarred

Cited by in corpus (10)

• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample
• Ray Tracing Simulations of Weak Lensing by Large-Scale Structure
• Controlling the False Discovery Rate in Astrophysical Data Analysis
• The DEEP2 galaxy redshift survey: the evolution of the blue fraction in groups and the field
• The DEEP2 Galaxy Redshift Survey: First results on galaxy groups
• The Clustering Properties of Lyman-Break Galaxies at Redshift z~3
• Effects of Sampling on Statistics of Large Scale Structure
• The DEEP2 Galaxy Redshift Survey: The Voronoi-Delaunay Method catalog of galaxy groups
• Evolution of the Cosmological Density Distribution Function: A New Analytical Model
• Sketch and Scale: Geo-distributed tSNE and UMAP