Galaxy Distribution Incompleteness Testing Using Self-Organizing Maps

TL;DR

This paper investigates how spectroscopic survey selection functions bias photometric redshift estimates, using self-organizing maps and simulations to evaluate calibration methods and their limitations.

Contribution

It introduces a local calibration testing approach with self-organizing maps to analyze the impact of selection functions on redshift inference and assesses bias mitigation techniques.

Findings

Removing underpopulated regions does not fully eliminate calibration biases.

Selection functions significantly affect redshift inference accuracy.

Bias correction methods need further development for effective mitigation.

Abstract

The calibration of redshift distributions for photometric samples using spectroscopic surveys is plagued by the difficulty in modelling the selection functions of spectroscopic surveys. In this work, we analyse how these selection functions impact redshift inference and quantify the induced biases using local calibration tests in photometry space. The study is carried out using simulations that mimic the radial selection function of a spectroscopic survey and an accompanying mock catalog of a photometric galaxy survey catalog. We use a self-organizing map to partition the photometry space and perform a local $\chi^2$ test to study the probability calibration of redshift inferences that use the spectroscopic data for calibration. The goal of this work is to investigate the effect of uncorrected selection functions in the calibration data on redshift prediction accuracy and critically discuss mitigation methods. In particular we test culling-based bias correction techniques, that aim to remove redshift calibration biases by identifying regions in photometry with few spectroscopic calibration data, and propose avenues for future research. We found that removing regions in color-magnitude space that are underpopulated with spectroscopic calibration data does not remove all biases in redshift inference induced by the selection function.