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Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR

Published online by Cambridge University Press:  01 July 2015

Stefano Cavuoti ,
Massimo Brescia  and
Giuseppe Longo
Stefano Cavuoti
Affiliation:
INAF - Astronomical Observatory of Capodimonte, I-80131, Napoli, Italy email: cavuoti@na.astro.it
Massimo Brescia
Affiliation:
INAF - Astronomical Observatory of Capodimonte, I-80131, Napoli, Italy email: cavuoti@na.astro.it
Giuseppe Longo
Affiliation:
Dept. of Physics, Naples University, Box I-80126 Napoli, Italy
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Abstract

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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.

Keywords

techniques: photometricgalaxies: distances and redshiftsmethods: data analysiscatalogs
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 307 - 309
Copyright
Copyright © International Astronomical Union 2015 

References

Brescia, M., Cavuoti, S., Paolillo, M., Longo, G., & Puzia, T. 2012, MNRAS 421 2, 1155Google Scholar
Brescia, M., Cavuoti, S., D'Abrusco, R., Longo, G., & Mercurio, A. 2013. Photometric redshifts for Quasars in multi band Surveys. ApJ, 772, 140Google Scholar
Brescia, M., Cavuoti, S., De Stefano, V., Longo, G. 2014a. A catalogue of photometric redshifts for the SDSS-DR9 galaxies, submitted to A&AGoogle Scholar
Brescia, M., Cavuoti, S., Longo, G., et al. 2014b. DAMEWARE: a web cyberinfrastructure for astrophysical data mining accepted by PASP (in press)CrossRefGoogle Scholar
Cavuoti, S., Brescia, M., Longo, G., Mercurio, A. 2012. Photometric redshifts with Quasi Newton Algorithm (MLPQNA). Results in the PHAT1 contest, A&A, Vol. 546, A13, 1–8Google Scholar
Cavuoti, S., Brescia, M., D'Abrusco, R., Longo, G., & Paolillo, M. 2014, Photometric classification of emission line galaxies with Machine Learning methods, Monthly Notices of the Royal Astronomical Society, Volume 437, Issue 1, p.968–975Google Scholar
Hoaglin, D.C., Mosteller, F., & Tukey, J.W. 1983. Understanding Robust and Exploratory Data Analysis, New York: WileyGoogle Scholar
Ilbert, O., Capak, P., Salvato, M., et al. 2009. Cosmos Photometric Redshifts with 30-bands for 2-deg2. The Astrophysical Journal 690, 1236CrossRefGoogle Scholar
Stehman, S. V. 1997. Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment 62, 1, 77–89CrossRefGoogle Scholar