Classification of Gravitational Waves from Black Hole-Neutron Star Mergers with Machine Learning
Ussipov N. Zhanabaev Z. Akhmetali A. Zaidyn M. Turlykozhayeva D. Akniyazova A. Namazbayev T.
2024Korean Space Science Society
Journal of Astronomy and Space Sciences
2024#41Issue 3149 - 158 pp.
This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black holeneutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron starblack hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.
classification , conditional information , gravitational waves , machine learning
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Department of Solid State Physics and Nonlinear Physics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Solid State Physics and Nonlinear Physics
10 лет помогаем публиковать статьи Международный издатель
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