Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-04T20:22:36.632Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic Source Classification in Digitised First Byurakan Survey

Published online by Cambridge University Press:  30 May 2017

Martin Topinka ,
Areg Mickaelian ,
Roberto Nesci  and
Corinne Rossi
Martin Topinka
Affiliation:
Dublin Institute for Advanced Studies, 31 Fitzwilliam place, Dublin 2, Ireland email: [email protected]
Areg Mickaelian
Affiliation:
Byurakan Astrophysical Observatory, Byurakan, Aragatzotn, AM 0213, Armenia
Roberto Nesci
Affiliation:
Universita di Roma ‘La Sapienza’, Piazzale A. Moro 2, 00185 Roma, Italy
Corinne Rossi
Affiliation:
Universita di Roma ‘La Sapienza’, Piazzale A. Moro 2, 00185 Roma, Italy
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The Digitised First Byurakan Survey (DFBS) provides low dispersion optical spectra for about 24 million sources. A two-step machine learning algorithm based on similarities to predefined templates is applied to select different classes of rare objects in the dataset automatically, for example late type stars, quasars and white dwarves. Identifying outliers from the groups of common astrophysical objects may lead to discovery of rare objects, such as gamma-ray burst afterglows.

Keywords

methods: statisticalastronomical data bases: surveys
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 186 - 190
Copyright
Copyright © International Astronomical Union 2017 

References

Mickaelian, A. M., Nesci, R., Rossi, C., et al., A&A, 464, 1177, 2007.Google Scholar
SDSS R12 webiste http://sdss.org Google Scholar
DFBS website http://ia2.oats.inaf.it Google Scholar
Bratsolis, E., Bellas-Velidis, I., Dapergolas, A., Kontizas, E. and Kontizas, M., Automatic extraction and classification of low-dispersion objective prism stellar spectra, A&A, Suppl. Ser., 2000 Google Scholar
Ivezic, Z., Connolly, A. J., VanderPlas, J. T., & Gray, A., Statistics, Data Mining and MachineLearning in Astronomy, Princeton University Press, 2014 Google Scholar