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 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
Photometric redshift estimation based on data mining with PhotoRApToR / Cavuoti, S.; Brescia, M.; De Stefano, V.; Longo, G.. - In: EXPERIMENTAL ASTRONOMY. - ISSN 0922-6435. - 39:1(2015), pp. 45-71. [10.1007/s10686-015-9443-4]
Photometric redshift estimation based on data mining with PhotoRApToR
Cavuoti, S.
;Brescia, M.;Longo, G.
2015
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 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 siteI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
