Photometric redshifts for the SDSS Data Release 12

TL;DR

This paper details a hybrid method for estimating photometric redshifts for over 200 million SDSS galaxies, combining local regression and spectral fitting, with high accuracy and comprehensive error estimates.

Contribution

It introduces a novel hybrid approach for photometric redshift estimation that integrates empirical local regression with spectral template fitting, applied to the SDSS DR12 data.

Findings

Achieved an average bias of 5.84e-5 in normalized redshift errors.

Standard deviation of redshift errors is 0.0205.

Outlier rate is 4.11% at 3 sigma.

Abstract

We present the methodology and data behind the photometric redshift database of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid technique, empirically estimating the redshift via local regression on a spectroscopic training set, then fitting a spectrum template to obtain K-corrections and absolute magnitudes. The SDSS spectroscopic catalog was augmented with data from other, publicly available spectroscopic surveys to mitigate target selection effects. The training set is comprised of $1,976,978$ galaxies, and extends up to redshift $z\approx 0.8$, with a useful coverage of up to $z\approx 0.6$. We provide photometric redshifts and realistic error estimates for the $208,474,076$ galaxies of the SDSS primary photometric catalog. We achieve an average bias of $\overline{\Delta z_{\mathrm{norm}}} = 5.84 \times 10^{-5}$, a standard deviation of $\sigma \left(\Delta z_{\mathrm{norm}}\right)=0.0205$, and a $3\sigma$ outlier rate of $P_o=4.11\%$ when cross-validating on our training set. The published redshift error estimates and photometric error classes enable the selection of galaxies with high quality photometric redshifts. We also provide a supplementary error map that allows additional, sophisticated filtering of the data.