2022
DOI: 10.48550/arxiv.2207.00591
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Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection

João Aveiro,
Felipe F. Freitas,
Márcio Ferreira
et al.

Abstract: We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision… Show more

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“…The development of fast ML alternatives to the computationally demanding Bayesian inference approaches has been remarkably established by [33] applying conditional variational autoencoders to BBH signals. ML approaches based on object detection have also been applied successfully in the task of detecting BNS coalescence events from the GW data of current detectors [34]. Motivated by this growing body of work, here the purpose is to study the potential to infer the NS EOS directly from simulated BNS inspiral signals using a ML approach.…”
Section: Introductionmentioning
confidence: 99%
“…The development of fast ML alternatives to the computationally demanding Bayesian inference approaches has been remarkably established by [33] applying conditional variational autoencoders to BBH signals. ML approaches based on object detection have also been applied successfully in the task of detecting BNS coalescence events from the GW data of current detectors [34]. Motivated by this growing body of work, here the purpose is to study the potential to infer the NS EOS directly from simulated BNS inspiral signals using a ML approach.…”
Section: Introductionmentioning
confidence: 99%