We present a methodology of network data analysis applied to the search for coincident burst excitations over a 24 h long data set collected by AURIGA, EXPLORER, NAUTILUS and Virgo detectors during September 2005. The search of candidate triggers was performed independently on each of the data sets from single detectors. We looked for two-fold time coincidences between these candidates using an algorithm optimized for a given population of sources and we calculated the efficiency of detection through injections of templated signal waveforms into the streams of data. To this end we have considered the case of signals shaped as damped sinusoids coming from the galactic center direction. Our method targets an optimal balance between high efficiency and low false alarm rate, aiming at setting confidence intervals as stringent as possible in terms of the rate of the selected source models.
Acernese, F., Alshourbagy, M., Amico, P., Antonucci, F., Aoudia, S., Astone, P., et al. (2008). First joint gravitational wave search by the AURIGA-EXPLORER-NAUTILUS-Virgo Collaboration. CLASSICAL AND QUANTUM GRAVITY, 25(20) [10.1088/0264-9381/25/20/205007].
First joint gravitational wave search by the AURIGA-EXPLORER-NAUTILUS-Virgo Collaboration
BASSAN, MASSIMO;COCCIA, EUGENIO;FAFONE, VIVIANA;M. Lorenzini;MOLETI, ARTURO;PIZZELLA, GUIDO;
2008-01-01
Abstract
We present a methodology of network data analysis applied to the search for coincident burst excitations over a 24 h long data set collected by AURIGA, EXPLORER, NAUTILUS and Virgo detectors during September 2005. The search of candidate triggers was performed independently on each of the data sets from single detectors. We looked for two-fold time coincidences between these candidates using an algorithm optimized for a given population of sources and we calculated the efficiency of detection through injections of templated signal waveforms into the streams of data. To this end we have considered the case of signals shaped as damped sinusoids coming from the galactic center direction. Our method targets an optimal balance between high efficiency and low false alarm rate, aiming at setting confidence intervals as stringent as possible in terms of the rate of the selected source models.Questo articolo è pubblicato sotto una Licenza Licenza Creative Commons
