Multiscale edge detection using-wavelet transform maxima provides a robust method to compress information in a transient signal. We apply this method to GRB time series data from the BATSE LADs. With this technique real temporal features manifest themselves as “edges” over a wide range of time scales, whereas Poisson noise seldom extends beyond the shortest time scales. This provides a method to quantify the variability, identify structures (e.g. FREDs), significantly suppress noise and compress the volume of data by as much as a factor of 50. There is a potential for further parameterizing a GRB time series by identifying and quantifying “edge” families that extend over a range of time scales.

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