A catalogue of photometric redshifts for the SDSS-DR9 galaxies

TL;DR

This paper presents a new machine learning method to estimate photometric redshifts for over 143 million SDSS-DR9 galaxies, achieving high accuracy and low outlier rates, thus providing a valuable resource for astronomical research.

Contribution

The paper introduces the MLPQNA model within DAMEWARE for photometric redshift estimation, improving accuracy and reducing outliers compared to previous methods.

Findings

Photometric redshifts for 143 million galaxies provided.

Achieved an uncertainty of sigma=0.023 with minimal bias.

Reduced the fraction of catastrophic outliers to about 5%.

Abstract

Accurate photometric redshifts for large samples of galaxies are among the main products of modern multiband digital surveys. Over the last decade, the Sloan Digital Sky Survey (SDSS) has become a sort of benchmark against which to test the various methods. We present an application of a new method to the estimation of photometric redshifts for the galaxies in the SDSS Data Release 9 (SDSS-DR9). Photometric redshifts for more than 143 million galaxies were produced and made available at the URL: http://dame.dsf.unina.it/catalog/DR9PHOTOZ/. The MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) model provided within the framework of the DAMEWARE (DAta Mining and Exploration Web Application REsource) is an interpolative method derived from machine learning models. The obtained redshifts have an overall uncertainty of sigma=0.023 with a very small average bias of about 3x10^-5, and a fraction of catastrophic outliers of about 5%. This result is slightly better than what was already available in the literature, particularly in terms of the smaller fraction of catastrophic outliers.