Completeness III: identifying characteristic systematics and evolution in galaxy redshift surveys

TL;DR

This paper develops and validates non-parametric completeness tests for galaxy redshift surveys, identifying various forms of survey incompleteness and luminosity evolution effects through robust estimators and error analysis.

Contribution

It introduces a comprehensive framework for detecting and characterizing survey incompleteness and luminosity evolution using the Tc and Tv estimators with error propagation.

Findings

Distinct signatures of incompleteness identified in real and mock surveys

Framework for detecting over-completeness in survey data

Method for constraining luminosity evolution models

Abstract

This paper continues our development of non-parametric tests for analysing the completeness in apparent magnitude of magnitude-redshift surveys. The purpose of this third and final paper in our completeness series is two-fold: firstly we explore how certain forms of incompleteness for a given flux-limited galaxy redshift survey would manifest themselves in the ROBUST Tc and Tv completeness estimators introduced in our earlier papers; secondly we provide a comprehensive error propagation for these estimators. By using both real surveys and Monte Carlo mock survey data, we have found distinct, characteristic behaviour of the Tc and Tv estimators which identify incompleteness in the form of e.g. missing objects within a particular magnitude range. Conversely we have identified signatures of `over' completeness, in cases where a survey contains a small region in apparent magnitude that may have too many objects relative to the rest of the data set. We also demonstrate how incompleteness resulting from luminosity evolution can be identified and provide a framework for using our estimators as a robust tool for constraining models of luminosity evolution. Finally we explore the error propagation for Tc andTv. This builds on Completeness II by allowing the definition of these estimators, and their errors, via an adaptive procedure that accounts for the effects of sampling error on the observed distribution of apparent magnitude and redshift in a survey.