Haar wavelets as a tool for the statistical characterization of variability
arXiv:1105.1309 · doi:10.1016/j.astropartphys.2011.03.006
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.
15 pages, 6 figures
References in corpus (5)
- Fast variability of TeV gamma-rays from the radio galaxy M 87
- The Discovery of gamma-Ray Emission From The Blazar RGB J0710+591
- Discovery of very high energy gamma rays from PKS 1424+240 and multiwavelength constraints on its redshift
- Discovery of Variability in the Very High Energy Gamma-Ray Emission of 1ES 1218+304 with VERITAS
- VERITAS 2008 - 2009 monitoring of the variable gamma-ray source M87