Photometric redshift estimation based on data mining with PhotoRApToR

Abstract

Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multilayer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and postprocessing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.

To appear on Experimental Astronomy, Springer, 20 pages, 15 figures

References in corpus (8)

• COSMOS Photometric Redshifts with 30-bands for 2-deg2
• Report of the Dark Energy Task Force
• Photometric Redshifts of Galaxies in COSMOS
• A catalogue of photometric redshifts for the SDSS-DR9 galaxies
• Feature importance for machine learning redshifts applied to SDSS galaxies
• Automated physical classification in the SDSS DR10. A catalogue of candidate Quasars
• Up to 100,000 reliable strong gravitational lenses in future dark energy experiments
• DAMEWARE: A web cyberinfrastructure for astrophysical data mining

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