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

• URL: http://arxiv.org/abs/2005.00220v2
• Date: Tue, 4 May 2021 19:29:32 GMT
• Title: Automatic Catalog of RRLyrae from $\sim$ 14 million VVV Light Curves: How far can we go with traditional machine-learning?
• Authors: Juan B. Cabral, Felipe Ramos, Sebasti\'an Gurovich and Pablo Granitto
• Abstract summary: The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the main goals of the VVV(X) surveys. Previous works introduced the use of Machine Learning (ML) methods for the variable star classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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