A catalogue of photometric redshifts for the SDSS-DR9 galaxies

Context. 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. Aims: 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. Methods: The Multi Layer Perceptron with Quasi Newton Algorithm (MLPQNA) model, provided within the framework of the DAta Mining and Exploration Web Application REsource (DAMEWARE), is an interpolative method derived from machine learning models. Results: The obtained redshifts have an overall uncertainty of sigma = 0.023 with a very small average bias of ~3 × 10-5, and a fraction of catastrophic outliers (|Deltaz| > 2sigma) of ~5%. This result is slightly better than what was already available in the literature in terms of the smaller fraction of catastrophic outliers as well.