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In the coming years gravitational-wave detectors will undergo a series of improvements, with an increase in their detection rate by about an order of magnitude. Routine detections of gravitational-wave signals promote novel astrophysical and fundamental theory studies, while simultaneously leading to an increase in the number of detections temporally overlapping with instrumentally- or environmentally-induced transients in the detectors (glitches), often of unknown origin. Indeed, this was the case for the very first detection by the LIGO and Virgo detectors of a gravitational-wave signal consistent with a binary neutron star coalescence, GW170817. A loud glitch in the LIGO-Livingston detector, about one second before the merger, hampered coincident detection (which was initially achieved solely with LIGO-Hanford data). Moreover, accurate source characterization depends on specific assumptions about the behavior of the detector noise that are rendered invalid by the presence of glitches. In this paper, we present the various techniques employed for the initial mitigation of the glitch to perform source characterization of GW170817 and study advantages and disadvantages of each mitigation method. We show that, despite the presence of instrumental noise transients louder than the one affecting GW170817, we are still able to produce unbiased measurements of the intrinsic parameters from simulated injections with properties similar to GW170817.
LIGO and Virgo recently completed searches for gravitational waves at their initial target sensitivities, and soon Advanced LIGO and Advanced Virgo will commence observations with even better capabilities. In the search for short duration signals, su
Gravitational waves are radiative solutions of space-time dynamics predicted by Einsteins theory of General Relativity. A world-wide array of large-scale and highly sensitive interferometric detectors constantly scrutinizes the geometry of the local
Transient non-gaussian noise in gravitational wave detectors, commonly referred to as glitches, pose challenges for inference of the astrophysical properties of detected signals when the two are coincident in time. Current analyses aim towards modeli
Existing coherent network analysis techniques for detecting gravitational-wave bursts simultaneously test data from multiple observatories for consistency with the expected properties of the signals. These techniques assume the output of the detector
In recent years, much work have studied the use of convolutional neural networks for gravitational-wave detection. However little work pay attention to whether the transient noise can trigger the CNN model or not. In this paper, we study the response