A machine learned classifier for RR Lyrae in the VVV survey
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Archivos
Fecha
2016-11
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
EDP Sciences
Nombre de Curso
Licencia CC
Licencia CC
Resumen
Variable stars of RR Lyrae type are a prime tool with which to obtain distances to old stellar populations in the Milky Way. One
of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the
Galactic Bulge. Owing to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae, and
particularly those of the more easily recognized type ab (i.e., fundamental-mode pulsators), from the 106−107 variables expected in
the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a Ks-band
VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier,
which were the choice of features, training set, selection of aperture, and family of classifiers. We find that the AdaBoost family
of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that
achieves a harmonic mean between false positives and false negatives of ≈7% for typical VVV light-curve sets. This performance is
estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.
Notas
Indexación: Scopus.
Palabras clave
Stars: Variables: RR Lyrae, Methods: Data Analysis, Methods: Statistical, Techniques: Photometric
Citación
Astronomy and Astrophysics. Volume 595. 1 November 2016. Article number A82
DOI
10.1051/0004-6361/201628700