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. © International Astronomical Union 2015.
Data-rich astronomy: Mining sky surveys with PhotoRApToR / Cavuoti, S.; Brescia, M.; Longo, G.. - 10:(2015), pp. 307-309. (Intervento presentato al convegno Statistical Challenges in 21st Century Cosmology (IAU S306) tenutosi a Lisbona (Portogallo) nel 25-29 maggio 2014) [10.1017/S1743921314013416].
Data-rich astronomy: Mining sky surveys with PhotoRApToR
Cavuoti, S.;Brescia, M.;Longo, G.
2015
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. © International Astronomical Union 2015.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
