Work

Gaiaeclipsing binary and multiple systems. Supervised classification and self-organizing maps

Year: 2017

Type: article

Abstract: Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys.

Source: Astronomy and Astrophysics

DOI: 10.1051/0004-6361/201629710

Authors: M. Süveges, F. Barblan, I. Lecoeur-Taïbi, A. Prša, B. Holl, L. Eyer, A. Kochoska, N. Mowlavi, L. Rimoldini

Institutions: University of Geneva, Villanova University, University of Ljubljana

Language: English

Topic: Stellar, planetary, and galactic studies

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W2591145506

FWCI: 0.749

Cites: 37

Cited by: 12

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