Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR
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.
- Publication:
-
Statistical Challenges in 21st Century Cosmology
- Pub Date:
- May 2014
- DOI:
- 10.1017/S1743921314013416
- arXiv:
- arXiv:1406.3192
- Bibcode:
- 2014IAUS..306..307C
- Keywords:
-
- techniques: photometric;
- galaxies: distances and redshifts;
- methods: data analysis;
- catalogs;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- proceedings of the IAU Symposium, Vol. 306, Cambridge University Press