2021 International Conference on Content-Based Multimedia Indexing (CBMI) 2021
DOI: 10.1109/cbmi50038.2021.9461893
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Gravitational-wave parameter inference using Deep Learning

Abstract: We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not… Show more

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Cited by 1 publication
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References 28 publications
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“…More recently, an ensemble algorithm of CNNs models for GW signal recognition was analyzed in [25]. Even without significant optimizations of the CNNs, their use in [26], showed that results obtained with DL algorithms were consistent to the large majority of the results published by the LIGO-Virgo Collaborations.…”
Section: Introductionmentioning
confidence: 58%
“…More recently, an ensemble algorithm of CNNs models for GW signal recognition was analyzed in [25]. Even without significant optimizations of the CNNs, their use in [26], showed that results obtained with DL algorithms were consistent to the large majority of the results published by the LIGO-Virgo Collaborations.…”
Section: Introductionmentioning
confidence: 58%