On Machine-learned Classification of Variable Stars with Sparse and Noisy Time-series Data

@inproceedings{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 Matthew Brewer and Arien Crellin-Quick and Justin Higgins and Rachel L Kennedy and Maxime Rischard},
  year={2011}
}
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… CONTINUE READING
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