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Variability Detection by Change-Point Analysis

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Statistical Challenges in Modern Astronomy V

Part of the book series: Lecture Notes in Statistics ((LNSP,volume 902))

Abstract

We describe a method to detect short-term variability based on the change-point analysis with filtering algorithm using local statistics. The use of cumulative sum scheme and bootstrap rank statistics as a means of detecting a series of change points is discussed. By applying this method to over 30,000 lightcurves from the MMT transit survey data, we found previously unknown evidences about stellar variability (including a total of 606 flare events, 18 eclipsing-like features, and 3 transit-like features). In particular, this approach will be effective in detecting non-periodic events in massive astronomical time series data.

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References

  1. Glicenstein, J. -F. 2001, in Microlensing 2000: A New Era of Microlensing Astrophysics, ed. by J. W. Menzies and P. D. Sackett. ASP Conf. Proc., Vol. 239, 28

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  2. Hartman, J. D., et al. 2008, ApJ, 675, 1233

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  3. Taylor, W. 2000, in Change-Point Analyzer 2.0 shareware program,http://www.variation.com/cpa

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Acknowledgements

This work is supported by Korea Institute of Science and Technology Information under the contract of the commissioned research project, Massive Astronomical Data Applications of Cloud Computation (KISTI-P11020).

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Authors and Affiliations

  1. Department of Astronomy, Yonsei University, Seoul, Korea

    Seo-Won Chang & Yong-Ik Byun

  2. Korea Institute of Science and Technology Information Supercomputing Center, Eoeun-dong 52, Yuseong, Daejeon, Korea

    Jaegyoon Hahm

Authors
  1. Seo-Won Chang
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  2. Yong-Ik Byun
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  3. Jaegyoon Hahm
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Corresponding author

Correspondence to Seo-Won Chang .

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Editors and Affiliations

  1. , Dept. Astronomy & Astrophysics, Pennsylvania State University, 518 Davey Lab, University Park, 16802, Pennsylvania, USA

    Eric D. Feigelson

  2. , Department of Statistics, Pennsylvania State University, 417C Thomas Building, University Park, 16802, Pennsylvania, USA

    G. Jogesh Babu

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© 2012 Springer Science+Business Media New York

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Chang, SW., Byun, YI., Hahm, J. (2012). Variability Detection by Change-Point Analysis. In: Feigelson, E., Babu, G. (eds) Statistical Challenges in Modern Astronomy V. Lecture Notes in Statistics(), vol 902. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3520-4_48

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