Efficient Use of Simultaneous Multi-Band Observations for Variable Star Analysis

  • Maria Süveges
  • Paul Bartholdi
  • Andrew Becker
  • Željko Ivezić
  • Mathias Beck
  • Laurent Eyer
Part of the Springer Series in Astrostatistics book series (SSIA, volume 2)


The luminosity changes of most types of variable stars are correlated in different wavelengths, and these correlations may be exploited for several purposes: variability detection, distinguishing microvariability from noise, period searches, or classification. Principal component analysis (PCA) is a simple and well-developed statistical tool to analyze correlated data. We will discuss its use on variable objects of Stripe 82 of the Sloan Digital Sky Survey, with the aim of identifying new RR Lyrae and SX Phoenicis-type candidates. The application is not straightforward because of different noise levels in the different bands, the presence of outliers that can be confused with real extreme observations, under- or overestimated errors, and the dependence of errors on magnitudes. These particularities require robust methods to be applied together with PCA. The results show that PCA is a valuable aid in variability analysis with multiband data.


Principal Component Analysis Point Cloud Variable Star Period Search Robust Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Adelman-McCarthy JK et al (2008) Astrophys J Suppl S 175:297Google Scholar
  2. 2.
    Bramich DM et al (2008) Mon Not Roy Astron Soc 386:77ADSCrossRefGoogle Scholar
  3. 3.
    Breiman L (2001) Machine Learning 45:5Google Scholar
  4. 4.
    Frieman JA et al (2008) Astron J 135:338ADSCrossRefGoogle Scholar
  5. 5.
  6. 6.
    Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New YorkMATHGoogle Scholar
  7. 7.
    Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer Science+Business Media, BerlinMATHCrossRefGoogle Scholar
  8. 8.
    McNamara DH (1997) Publ Astron Soc Pac 109:1221ADSCrossRefGoogle Scholar
  9. 9.
    McNamara DH, Clementini G, Marconi M (2007) Astron J 133:2752ADSCrossRefGoogle Scholar
  10. 10.
    Rousseeuw PJ (1985) Mathematical statistics and applications. In: Grossmann W, Pflug G, Vincze I, Wertz W (eds), vol. B. Reidel, Dordrecht, p 28Google Scholar
  11. 11.
  12. 12.
    Sesar B et al (2007) Astron J 134:2236ADSCrossRefGoogle Scholar
  13. 13.
    Sesar B et al (2010) Astrophys J 708:717ADSCrossRefGoogle Scholar
  14. 14.
    Zechmeister M, Kürster M (2009) Astron Astrophys 496:577ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Maria Süveges
    • 1
  • Paul Bartholdi
    • 2
  • Andrew Becker
    • 3
  • Željko Ivezić
    • 3
  • Mathias Beck
    • 1
  • Laurent Eyer
    • 2
  1. 1.ISDC Data Centre for AstrophysicsAstronomical Observatory of GenevaVersoixSwitzerland
  2. 2.Astronomical Observatory of GenevaSauvernySwitzerland
  3. 3.University of WashingtonSeattleUSA

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