Cavuoti, S.S.CavuotiBRESCIA, MassimoMassimoBRESCIADe Stefano, V.V.De StefanoLongo, G.G.Longo2020-03-132020-03-1320150922-6435http://hdl.handle.net/20.500.12386/23220Photometric 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 multi-layer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and post-processing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site. <P />STAMPAenArticle10.1007/s10686-015-9443-42-s2.0-84945301895000352156800005https://link.springer.com/article/10.1007%2Fs10686-015-9443-42015ExA....39...45CFIS/05 - ASTRONOMIA E ASTROFISICAScienze Fisiche Settori ERC (ERC) di riferimento::PE9 Universe sciences: astro-physics/chemistry/biology; solar systems; stellar, galactic and extragalactic astronomy, planetary systems, cosmology, space science, instrumentation