Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR. Issue Volume 10:Issue S306(2014) (1st July 2015)

• Record Type: Journal Article
• Authors: Cavuoti, Stefano; Brescia, Massimo; Longo, Giuseppe
• Editors: Heavens, A. F.; Starck, J.-L.; Krone-Martins, A.
• Is Part Of: Proceedings of the International Astronomical Union. Volume 10:Issue S306(2014)
• Page Start: 307
• Page End: 309
• Publication Date: 2015-07-01
• Subjects: techniques: photometric, -- galaxies: distances and redshifts, -- methods: data analysis, -- catalogs; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921314013416
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become a data-rich science. New automatic methods largely based on machine learning are needed to cope with such data tsunami. We present some results in the fields of photometric redshifts and galaxy classification, obtained using the MLPQNA algorithm available in the DAMEWARE (Data Mining and Web Application Resource) for the SDSS galaxies (DR9 and DR10). We present PhotoRApToR (Photometric Research Application To Redshift): a Java based desktop application capable to solve regression and classification problems and specialized for photo-z estimation.