Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning

TL;DR

This paper introduces rejection criteria based on PDF shape descriptors to improve photometric redshift accuracy in sky surveys, effectively reducing outliers while maintaining sample completeness.

Contribution

It proposes a novel method to reject outliers using PDF shape metrics, enhancing photometric redshift precision without significantly losing data.

Findings

Outlier fraction reduced by approximately 75%

NMAD improved by about 6%

Sample completeness preserved at around 93%

Abstract

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of identifying and rejecting potential outliers, can increase the precision of zphot estimates and of their cumulative PDF, without sacrificing much in terms of completeness of the sample. We provide a way to assess rejection through proper cuts on the shape descriptors of a PDF, such as the width and the height of the maximum PDF's peak. In this work we tested these rejection criteria to galaxies with photometry extracted from the Kilo Degree Survey (KiDS) ESO Data Release 4, proving that such approach could lead to significant improvements to the zphot quality: e.g., for the clipped sample showing the best trade-off between precision and completeness, we achieve a reduction in outliers fraction of $\simeq 75\%$ and an improvement of $\simeq 6\%$ for NMAD, with respect to the original data set, preserving the $\simeq 93\%$ of its content.