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Observation results by the TAMA300 detector on gravitational wave bursts from stellar-core collapses

We present data-analysis schemes and results of observations with the TAMA300 gravitational-wave detector, targeting burst signals from stellar-core collapse events. In analyses for burst gravitational waves, the detection and fake-reduction schemes are different from well-investigated ones for a ch...

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Published in:arXiv.org 2004-11
Main Authors: Ando, Masaki, Arai, Koji, Aso, Youichi, Beyersdorf, Peter, Hayama, Kazuhiro, Iida, Yukiyoshi, Kanda, Nobuyuki, Kawamura, Seiji, Kondo, Kazuhiro, Mio, Norikatsu, Miyoki, Shinji, Moriwaki, Shigenori, Nagano, Shigeo, Numata, Kenji, Sato, Shuichi, Somiya, Kentaro, Tagoshi, Hideyuki, Takahashi, Hirotaka, Takahashi, Ryutaro, Tatsumi, Daisuke, Tsunesada, Yoshiki, Zong-Hong, Zhu, the TAMA collaboration
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Language:English
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Summary:We present data-analysis schemes and results of observations with the TAMA300 gravitational-wave detector, targeting burst signals from stellar-core collapse events. In analyses for burst gravitational waves, the detection and fake-reduction schemes are different from well-investigated ones for a chirp-wave analysis, because precise waveform templates are not available. We used an excess-power filter for the extraction of gravitational-wave candidates, and developed two methods for the reduction of fake events caused by non-stationary noises of the detector. These analysis schemes were applied to real data from the TAMA300 interferometric gravitational wave detector. As a result, fake events were reduced by a factor of about 1000 in the best cases. The resultant event candidates were interpreted from an astronomical viewpoint. We set an upper limit of 2.2x10^3 events/sec on the burst gravitational-wave event rate in our Galaxy with a confidence level of 90%. This work sets a milestone and prospects on the search for burst gravitational waves, by establishing an analysis scheme for the observation data from an interferometric gravitational wave detector.
ISSN:2331-8422
DOI:10.48550/arxiv.0411027