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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2312.04855 |
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| _version_ | 1866916117754150912 |
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| author | Sasaoka, Seiya Koyama, Naoki Dominguez, Diego Sakai, Yusuke Somiya, Kentaro Omae, Yuto Takahashi, Hirotaka |
| author_facet | Sasaoka, Seiya Koyama, Naoki Dominguez, Diego Sakai, Yusuke Somiya, Kentaro Omae, Yuto Takahashi, Hirotaka |
| contents | Recent advancements in gravitational wave astronomy have seen the application of convolutional neural networks (CNNs) in signal detection from compact binary coalescences. This study presents a comparative analysis of two CNN architectures: one-dimensional (1D) and two-dimensional (2D) along with an ensemble model combining both. We trained these models to detect gravitational wave signals from binary black hole (BBH) mergers, neutron star-black hole (NSBH) mergers, and binary neutron star (BNS) mergers within real detector noise. Our investigation entailed a comprehensive evaluation of the detection performance of each model type across different signal classes. To understand the models' decision-making processes, we employed feature map visualization and attribution analysis. The findings revealed that while the 1D model showed superior performance in detecting BBH signals, the 2D model excelled in identifying NSBH and BNS signals. Notably, the ensemble model outperformed both individual models across all signal types, demonstrating enhanced detection capabilities. Additionally, input feature visualization indicated distinct areas of focus in the data for the 1D and 2D models, emphasizing the effectiveness of their combination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_04855 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences Sasaoka, Seiya Koyama, Naoki Dominguez, Diego Sakai, Yusuke Somiya, Kentaro Omae, Yuto Takahashi, Hirotaka General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Recent advancements in gravitational wave astronomy have seen the application of convolutional neural networks (CNNs) in signal detection from compact binary coalescences. This study presents a comparative analysis of two CNN architectures: one-dimensional (1D) and two-dimensional (2D) along with an ensemble model combining both. We trained these models to detect gravitational wave signals from binary black hole (BBH) mergers, neutron star-black hole (NSBH) mergers, and binary neutron star (BNS) mergers within real detector noise. Our investigation entailed a comprehensive evaluation of the detection performance of each model type across different signal classes. To understand the models' decision-making processes, we employed feature map visualization and attribution analysis. The findings revealed that while the 1D model showed superior performance in detecting BBH signals, the 2D model excelled in identifying NSBH and BNS signals. Notably, the ensemble model outperformed both individual models across all signal types, demonstrating enhanced detection capabilities. Additionally, input feature visualization indicated distinct areas of focus in the data for the 1D and 2D models, emphasizing the effectiveness of their combination. |
| title | Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2312.04855 |