Detecting quasars in large-scale astronomical surveys

Fabian Gieseke*, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomanns, Ralf Jürgen Dettmar, Oliver Kramer, Jan Vahrenhold

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

13 Citationer (Scopus)

Abstract

We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.

OriginalsprogEngelsk
TitelProceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Antal sider6
ForlagIEEE
Publikationsdato2010
Sider352-357
Artikelnummer5708856
ISBN (Trykt)978-0-7695-4300-0
DOI
StatusUdgivet - 2010
Udgivet eksterntJa
Begivenhed9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, USA
Varighed: 12 dec. 201014 dec. 2010

Konference

Konference9th International Conference on Machine Learning and Applications, ICMLA 2010
Land/OmrådeUSA
ByWashington, DC
Periode12/12/201014/12/2010
SponsorAssociation for Machine Learning and Applications, IEEE, California State University Bakersfield, Wayne State University

Citationsformater