Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/23220
Title: | Photometric redshift estimation based on data mining with PhotoRApToR | Authors: | Cavuoti, S. BRESCIA, Massimo De Stefano, V. Longo, G. |
Issue Date: | 2015 | Journal: | EXPERIMENTAL ASTRONOMY | Number: | 39 | Issue: | 1 | First Page: | 45 | 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 site. <P /> | URI: | http://hdl.handle.net/20.500.12386/23220 | URL: | https://link.springer.com/article/10.1007%2Fs10686-015-9443-4 | ISSN: | 0922-6435 | DOI: | 10.1007/s10686-015-9443-4 | Bibcode ADS: | 2015ExA....39...45C | Fulltext: | open |
Appears in Collections: | 1.01 Articoli in rivista |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Experimental posprint.pdf | postprint | 3.33 MB | Adobe PDF | View/Open |
Cavuoti2015_Article_PhotometricRedshiftEstimationB.pdf | [administrators only] | 3.03 MB | Adobe PDF |
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