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Register a new userImproved methods for detecting gravitational waves associated with short gamma-ray bursts
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In the era of second generation ground-based gravitational wave detectors, short gamma-ray bursts (GRBs) will be among the most promising astrophysical events for joint electromagnetic and gravitational wave observation. A targeted search for gravitational wave compact binary merger signals in coincidence with short GRBs was developed and used to analyze data from the first generation LIGO and Virgo instruments. In this paper, we present improvements to this search that enhance our ability to detect gravitational wave counterparts to short GRBs. Specifically, we introduce an improved method for estimating the gravitational wave background to obtain the event significance required to make detections; implement a method of tiling extended sky regions, as required when searching for signals associated to poorly localized GRBs from Fermi Gamma-ray Burst Monitor or the InterPlanetary Network; and incorporate astrophysical knowledge about the beaming of GRB emission to restrict the search parameter space. We describe the implementation of these enhancements and demonstrate how they improve the ability to observe binary merger gravitational wave signals associated with short GRBs.
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The cosmological origin of $gamma$-ray bursts (GRBs) is now commonly accepted and, according to several models for the central engine, GRB sources should also emit at the same time gravitational waves bursts (GWBs). We have performed two correlation searches between the data of the resonant gravitational wave detector AURIGA and GRB arrival times collected in the BATSE 4B catalog. No correlation was found and an upper limit bbox{$h_{text{RMS}} leq 1.5 times 10^{-18}$} on the averaged amplitude of gravitational waves associated with $gamma$-ray bursts has been set for the first time.
We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.
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