A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6

TL;DR

This paper provides a comprehensive catalog of galaxy photometric redshifts for SDSS DR6, utilizing neural networks and error estimation methods, enabling large-scale galaxy distance measurements with quantified uncertainties.

Contribution

The paper introduces a new galaxy photometric redshift catalog for SDSS DR6 using neural networks and error estimation, validated on extensive spectroscopic data.

Findings

68% of galaxies have photo-z errors below 0.021 or 0.024

Photometric redshifts are accurate for large galaxy samples

The catalog covers ~77 million galaxies with quantified uncertainties.

Abstract

We present and describe a catalog of galaxy photometric redshifts (photo-z's) for the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6). We use the Artificial Neural Network (ANN) technique to calculate photo-z's and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for ~ 77 million objects classified as galaxies in DR6 with r < 22. The photo-z and photo-z error estimators are trained and validated on a sample of ~ 640,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by SDSS, 2SLAQ, CFRS, CNOC2, TKRS, DEEP, and DEEP2. For the two best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than sigma_{68} =0.021 or $0.024. After presenting our results and quality tests, we provide a short guide for users accessing the public data.