Automatic Catalog of RRLyrae from $\sim$ 14 million VVV Light Curves: How far can we go with traditional machine-learning?

Abstract

The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the main goals of the VVV(X) surveys. The overwhelming number of sources under analysis request the use of automatic procedures. In this context, previous works introduced the use of Machine Learning (ML) methods for the variable star classification. Our goal is the development and analysis of an automatic procedure, based on ML, for the identification of RRLs in the VVV Survey. This procedure will be use to generate reliable catalogs integrated over several tiles in the survey. After the reconstruction of light-curves, we extract a set of period and intensity-based features. We use for the first time a new subset of pseudo color features. We discuss all the appropriate steps needed to define our automatic pipeline: selection of quality measures; sampling procedures; classifier setup and model selection. As final result, we construct an ensemble classifier with an average Recall of 0.48 and average Precision of 0.86 over 15 tiles. We also make available our processed datasets and a catalog of candidate RRLs. Perhaps most interestingly, from a classification perspective based on photometric broad-band data, is that our results indicate that Color is an informative feature type of the RRL that should be considered for automatic classification methods via ML. We also argue that Recall and Precision in both tables and curves are high quality metrics for this highly imbalanced problem. Furthermore, we show for our VVV data-set that to have good estimates it is important to use the original distribution more than reduced samples with an artificial balance. Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step, and that most errors in the identification of RRLs are related to low quality observations of some sources or to the difficulty to resolve the RRL-C type given the date.

References in corpus (15)

• The NumPy array: a structure for efficient numerical computation
• The Gaia mission
• Achieving better than 1 minute accuracy in the Heliocentric and Barycentric Julian Dates
• The VISTA ZYJHKs Photometric System: Calibration from 2MASS
• Mapping the Milky Way bulge at high resolution: the 3D dust extinction, CO, and X factor maps
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• K2 Variable Catalogue: Variable Stars and Eclipsing Binaries in K2 Campaigns 1 and 0
• Machine learning search for variable stars
• A machine learned classifier for RR Lyrae in the VVV survey
• Metallicity Analysis of MACHO Galactic Bulge RR0 Lyrae stars from their Lightcurves
• Imbalance Learning for Variable Star Classification
• The Extinction Toward the Galactic Bulge from RR Lyrae Stars
• Blazhko effect in the Galactic bulge fundamental mode RR~Lyrae stars I: Incidence rate and differences between modulated and non-modulated stars
• Toward an internally consistent astronomical distance scale
• Bulge RR Lyrae stars in the VVV tile $\text{b201}$

Cited by in corpus (5)

• Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning
• A microlensing search of 700 million VVV light curves
• VVV catalog of ab-type RR Lyrae in the inner Galactic bulge
• Light Curve Classification with DistClassiPy: a new distance-based classifier
• Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV