Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR

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 fiel...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2014-06
Main Authors: Cavuoti, Stefano, Brescia, Massimo, Longo, Giuseppe
Format: Article
Language:English
Subjects:
Algorithms
Applications programs
Astronomy
Classification
Data mining
Galaxies
Machine learning
Photometry
Red shift
Sky surveys (astronomy)
Telescopes
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422