The SDSS Coadd: A Galaxy Photometric Redshift Catalog

TL;DR

This paper introduces a comprehensive galaxy photometric redshift catalog for SDSS Coadd data, utilizing neural networks and error estimation techniques, covering 13 million galaxies with validated accuracy and providing a user guide.

Contribution

It presents a new large-scale galaxy photo-z catalog using ANN and NNE methods, trained on multiple surveys, with detailed validation and error estimation.

Findings

68% of galaxies have photo-z errors below 0.031

The catalog covers approximately 13 million galaxies

Validated accuracy using multiple spectroscopic surveys

Abstract

We present and describe a catalog of galaxy photometric redshifts (photo-z's) for the Sloan Digital Sky Survey (SDSS) Coadd Data. 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 $\sim$ 13 million objects classified as galaxies in the coadd with $r < 24.5$. The photo-z and photo-z error estimators are trained and validated on a sample of $\sim 83,000$ galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Deep Extragalactic Evolutionary Probe Data Release 3(DEEP2 DR3), the VIsible imaging Multi-Object Spectrograph - Very Large Telescope Deep Survey (VVDS) and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than $\sigma_{68} =0.031$. After presenting our results and quality tests, we provide a short guide for users accessing the public data.