EAZY: A Fast, Public Photometric Redshift Code

TL;DR

EAZY is a fast, flexible photometric redshift code that uses semi-analytical models for templates, providing reliable redshift estimates without relying on spectroscopic training data.

Contribution

It introduces a novel template error function and a redshift quality parameter, improving accuracy and reliability of photometric redshifts without spectroscopic training.

Findings

Achieves a 1-sigma scatter of 0.034 in dz/(1+z) on deep field datasets.

Performs well on public datasets without spectroscopic training.

Provides updated photometric redshift catalogs for major surveys.

Abstract

We describe a new program for determining photometric redshifts, dubbed EAZY. The program is optimized for cases where spectroscopic redshifts are not available, or only available for a biased subset of the galaxies. The code combines features from various existing codes: it can fit linear combinations of templates, it includes optional flux- and redshift-based priors, and its user interface is modeled on the popular HYPERZ code. A novel feature is that the default template set, as well as the default functional forms of the priors, are not based on (usually highly biased) spectroscopic samples, but on semi-analytical models. Furthermore, template mismatch is addressed by a novel rest-frame template error function. This function gives different wavelength regions different weights, and ensures that the formal redshift uncertainties are realistic. We introduce a redshift quality parameter, Q_z, that provides a robust estimate of the reliability of the photometric redshift estimate. Despite the fact that EAZY is not "trained" on spectroscopic samples, the code (with default parameters) performs very well on existing public datasets. For K-selected samples in CDF-South and other deep fields we find a 1-sigma scatter in dz/(1+z) of 0.034, and we provide updated photometric redshift catalogs for the FIRES, MUSYC, and FIREWORKS surveys.