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- W3100897877 abstract "We present a new wavelet packet decomposition method meant to gravitational wave detection. An issue in wavelet analysis is the choice of time-frequency resolution in order to best represent and reduce data, while in quest of a signal of unknown shape like a burst. In other wavelet methods currently employed, like the LIGO WaveBurst for instance, the analysis is performed at some trial resolutions. We propose a decomposition which automatically selects the best resolution at any frequency. The core of this best resolution selection criterion is the minimization of a function on the wavelet packet coefficients domain, named entropy, like the entropy function used in information theory. As a qualitative application we show how a multiresolution time-frequency scalogram looks like for a sample signal injected over Gaussian noise. As a quantitative application of the method, we use the wavelet packet decomposition to perform a non-linear filter of the data by rejecting the wavelet coefficients under a given threshold, analogously to what is done by the WaveBurst algorithm. Taking coincidences between the events obtained by WaveBurst on the original data and on the data filtered after the wavelet packet decomposition, the false alarm rate has been lowered with negligible effects on the efficiency." @default.
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- W3100897877 date "2008-07-01" @default.
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- W3100897877 title "Adaptive multiresolution for wavelet analysis" @default.
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- W3100897877 doi "https://doi.org/10.1088/1742-6596/122/1/012036" @default.
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