Photometric Redshift Error Estimators

TL;DR

This paper introduces new training-set based methods for estimating photometric redshift errors, demonstrating their robustness and effectiveness in reducing catastrophic failures in large astronomical surveys.

Contribution

The paper proposes novel training-set based error estimators for photometric redshifts, improving error quantification and failure identification in large survey data.

Findings

Training-set based estimators provide robust, unbiased error estimates.

Culling high-error objects reduces catastrophic photo-z failures.

Methods tested on SDSS and DES simulation data.

Abstract

Photometric redshift (photo-z) estimates are playing an increasingly important role in extragalactic astronomy and cosmology. Crucial to many photo-z applications is the accurate quantification of photometric redshift errors and their distributions, including identification of likely catastrophic failures in photo-z estimates. We consider several methods of estimating photo-z errors and propose new training-set based error estimators based on spectroscopic training set data. Using data from the Sloan Digital Sky Survey and simulations of the Dark Energy Survey as examples, we show that this method provides a robust, relatively unbiased estimate of photo-z errors. We show that culling objects with large, accurately estimated photo-z errors from a sample can reduce the incidence of catastrophic photo-z failures.