ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA

@article{Richards2011ONMC,
  title={ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA},
  author={Joseph W. Richards and Dan L. Starr and Nathaniel R. Butler and Joshua S. Bloom and John M. Brewer and Arien Crellin-Quick and Justin Higgins and Rachel Kennedy and Maxime Rischard},
  journal={The Astrophysical Journal},
  year={2011},
  volume={733}
}
With the coming data deluge from synoptic surveys, there is a need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly observed variables based on small numbers of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics… 
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238 Citations
Highly Influential Citations
25
Background Citations
87
Methods Citations
98
Results Citations
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238 Citations

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