Automated classification of eclipsing binary systems in the VVV Survey
No hay miniatura disponible
Archivos
Fecha
2023-03
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Oxford University Press
Nombre de Curso
Licencia CC
Licencia CC
Resumen
With the advent of large-scale photometric surveys of the sky, modern science witnesses the dawn of big data astronomy, where automatic handling and discovery are paramount. In this context, classification tasks are among the key capabilities a data reduction pipeline must possess in order to compile reliable data sets, to accomplish data processing with an efficiency level impossible to achieve by means of detailed processing and human intervention. The VISTA Variables of the Vía Láctea Survey, in the southern part of the Galactic disc, comprises multiepoch photometric data necessary for the potential discovery of variable objects, including eclipsing binary systems (EBs). In this study, we use a recently published catalogue of one hundred EBs, classified by fine-tuning theoretical models according to contact, detached, or semidetached classes belonging to the tile d040 of the VVV. We describe the method implemented to obtain a supervised machine-learning model, capable of classifying EBs using information extracted from the light curves of variable object candidates in the phase space from tile d078. We also discuss the efficiency of the models, the relative importance of the features and the future prospects to construct an extensive data base of EBs in the VVV survey. © 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
Notas
Indexación: Scopus
Palabras clave
Binaries: Eclipsing, Infrared: Stars, Methods: Data Analysis, Methods: Statistical
Citación
Monthly Notices of the Royal Astronomical Society. Volume 520, Issue 1, Pages 828 - 8381. March 2023