Variability detection by change-point analysis

Seo Won Chang, Yong Ik Byun, Jaegyoon Hahm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationInformation Systems Development
Subtitle of host publicationReflections, Challenges and New Directions
Pages491-493
Number of pages3
DOIs
Publication statusPublished - 2013
Event20th International Conference on Information Systems Development: Reflections, Challenges and New Directions, ISD 2011 - Edinburgh, United Kingdom
Duration: 2011 Aug 242011 Aug 26

Publication series

NameInformation Systems Development: Reflections, Challenges and New Directions

Other

Other20th International Conference on Information Systems Development: Reflections, Challenges and New Directions, ISD 2011
CountryUnited Kingdom
CityEdinburgh
Period11/8/2411/8/26

All Science Journal Classification (ASJC) codes

  • Information Systems

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  • Cite this

    Chang, S. W., Byun, Y. I., & Hahm, J. (2013). Variability detection by change-point analysis. In Information Systems Development: Reflections, Challenges and New Directions (pp. 491-493). (Information Systems Development: Reflections, Challenges and New Directions). https://doi.org/10.1007/978-1-4614-3520-4-48