Physics
Scientific paper
Jun 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004pthph.111..807h&link_type=abstract
Progress of Theoretical Physics, Vol. 111, No. 6, pp. 807-819.
Physics
3
Scientific paper
This paper proposes a wavelet-based method to detect Type II supernovae signals, which are burst-like signals that are poorly-predicted. The proposed method can approximate the burst signals non-parametrically, and the estimator is nearly optimal even, if we do not use the prior information about the waveforms. Therefore it is believed that this method is suitable for detecting such burst signals. In order to study the performance of this method, we demonstrate the detection of supernovae signals subject to Gaussian white noise using the catalog of waveforms provided recently by Dimmelmeier et al. as target supernovae signals, and we compare its performance with that of the Wiener estimator. We obtain two main results: First, our method is superior to that of the Wiener filter for all the supernovae signals in the catalog. Second, the performance of our method does not depend on the duration of the bursts. By contrast, the performance of the Wiener estimator becomes worse as the duration of the burst signals decreases.
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