May 2022
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29 Reads
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1 Citation
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Publications (3)
June 2021
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102 Reads
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69 Citations
February 2020
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63 Reads
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48 Citations
Based on the prior O1–O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e. they will be collected at times when only one detector in the network is operating in observing mode. Searches for gravitational-wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals. If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherent WaveBurst pipeline. We show that the method may be used to discriminate Galactic gravitational-wave supernova signals from noise transients, decrease the false alarm rate of the search, and improve the supernova detection reach of the detectors.
Citations (3)
... Accelerated masses produce Gravitational Waves (GWs), which are disturbances or ripples in the curvature of spacetime that move as waves away from their source at the speed of light [6]. These cosmic ripples would move through space and time, bringing with them clues to the nature of gravity and information about their origins [26]. ...
- Citing Article
May 2022
... The LIGO-VIRGO-KAGRA [10][11][12] collaboration has finished three observing runs and released the results in the third Gravitational-Waves Transient Catalog (GWTC-3) [13][14][15]. In total, the collaboration has observed more than 90 events since 2015, when it began operating. ...
- Citing Article
June 2021
... In this line of thought, several authors have proposed to benefit of the particularity of CCSN signal for detection [22][23][24][25][26][27][28][29][30], and regression [31,32] tasks using Machine Learning (ML). ML has been successful in a variety of applications and it has emerged as a novel tool in the GW field (see [33] for a comprehensive review). ...
- Citing Article
- Full-text available
February 2020