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Haar wavelets as a tool for the statistical characterization of variability

Authors
  • Price, Ryan
  • Vincent, Stephane
  • LeBohec, Stephan
Type
Published Article
Publication Date
May 06, 2011
Submission Date
May 06, 2011
Identifiers
DOI: 10.1016/j.astropartphys.2011.03.006
Source
arXiv
License
Yellow
External links

Abstract

In the field of gamma-ray astronomy, irregular and noisy datasets make difficult the characterization of light-curve features in terms of statistical significance while properly accounting for trial factors associated with the search for variability at different times and over different timescales. In order to address these difficulties, we propose a method based on the Haar wavelet decomposition of the data. It allows statistical characterization of possible variability, embedded in a white noise background, in terms of a confidence level. The method is applied to artificially generated data for characterization as well as to the the very high energy M87 light curve recorded with VERITAS in 2008 which serves here as a realistic application example.

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