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

@article{Cabral2020AutomaticCO,
  title={Automatic Catalog of RRLyrae from ~ 14 million VVV Light Curves: How far can we go with traditional machine-learning?},
  author={Juan B. Cabral and F. Ramos and Sebasti{\'a}n Gurovich and Pablo M. Granitto},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.00220}
}
Context. The creation of a 3D map of the bulge using RR Lyrae (RRL) is one of the main goals of the VISTA Variables in the Via Lactea Survey (VVV) and VVV(X) surveys. The overwhelming number of sources undergoing analysis undoubtedly requires the use of automatic procedures. In this context, previous studies have introduced the use of machine learning (ML) methods for the task of variable star classification. Aims. Our goal is to develop and test an entirely automatic ML-based procedure for the… 
1 Citations
Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV
TLDR
A new strategy to cope with small changes on the data over long angular distances or long periods of time, which cannot be easily detected by statistical methods, is developed, and Drifting Features can be efficiently identified using ML methods.

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