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.
@article{arxiv.1501.06506,
title = {Photometric redshift estimation based on data mining with PhotoRApToR},
author = {Stefano Cavuoti and Massimo Brescia and Virgilio De Stefano and Giuseppe Longo},
journal= {arXiv preprint arXiv:1501.06506},
year = {2016}
}
Comments
To appear on Experimental Astronomy, Springer, 20 pages, 15 figures