On machine-learned classification of variable stars with sparse and noisy time-series data

Joseph W. Richards, Dan L. Starr, Nathaniel R. Butler, Joshua S. Bloom, John M. Brewer, Arien Crellin-Quick, Justin Higgins, Rachel Kennedy, Maxime Rischard

Research output: Contribution to journalArticle

145 Scopus citations

Abstract

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 (features), detail methods to robustly estimate periodic features, introduce tree-ensemble methods for accurate variable-star classification, and show how to rigorously evaluate a classifier using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% error rate using the random forest (RF) classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. The RF classifier is superior to other methods in terms of accuracy, speed, and relative immunity to irrelevant features; the RF can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which reduces the catastrophic error rate from 8% to 7.8%. Excluding low-amplitude sources, the overall error rate improves to 14%, with a catastrophic error rate of 3.5%.

Original languageEnglish (US)
Article number10
JournalAstrophysical Journal
Volume733
Issue number1
DOIs
StatePublished - May 20 2011

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Keywords

  • methods: data analysis
  • methods: statistical
  • stars: variables: general
  • techniques: photometric

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

Richards, J. W., Starr, D. L., Butler, N. R., Bloom, J. S., Brewer, J. M., Crellin-Quick, A., Higgins, J., Kennedy, R., & Rischard, M. (2011). On machine-learned classification of variable stars with sparse and noisy time-series data. Astrophysical Journal, 733(1), [10]. https://doi.org/10.1088/0004-637X/733/1/10