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.
- Publication Date:
- NSF-PAR ID:
- 10303193
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 1
- Issue:
- 1
- Page Range or eLocation-ID:
- Article No. 015005
- ISSN:
- 2632-2153
- Publisher:
- IOP Publishing
- Sponsoring Org:
- National Science Foundation
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