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Denoising and detection for binary black hole gravitational waves in the context of the Einstein Telescope.

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Název: Denoising and detection for binary black hole gravitational waves in the context of the Einstein Telescope.
Autoři: Ma, CunLiang, Liu, ZeHua, Gao, ZhiFu, Cao, Zhoujian, Jia, MingZhen, Wei, Kai, Dai, TanMing, Wu, JunQin
Zdroj: SCIENCE CHINA Physics, Mechanics & Astronomy; Jul2025, Vol. 68 Issue 7, p1-17, 17p
Abstrakt: With the advent of third-generation (3G) ground-based gravitational wave (GW) detectors, such as the Einstein Telescope (ET), we anticipate a substantial enhancement in sensitivity across a wide frequency range. The machine learning approach for GW search necessitates an update to address the challenges posed by data features that deviate from those of 2G detectors. In this paper, we introduce a novel GW search pipeline specifically designed for 3G ground-based detectors like ET. Our pipeline leverages three types of deep learning models: an envelope extraction model, a denoising model, and an astrophysical origin discrimination model. Additionally, we propose a signal consistency test across multiple detectors. Given that denoising results vary among different detectors, we present a new method for selecting the optimal waveform. This selected waveform serves as a template for estimating the signal-to-noise ratio (SNR) of strain data from all detectors. Furthermore, if 3G detectors operate alongside 2G detectors, the templates derived from 3G detector data can be utilized to predict the SNR for 2G detectors, significantly reducing the computational burden of GW searches for the latter. We also assess the robustness of our method when applied to data containing binary neutron star (BNS) foreground noise. We believe that the proposed method holds promise for detecting BBH events in future 3G detectors. [ABSTRACT FROM AUTHOR]
Copyright of SCIENCE CHINA Physics, Mechanics & Astronomy is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Denoising and detection for binary black hole gravitational waves in the context of the Einstein Telescope.
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  Data: SCIENCE CHINA Physics, Mechanics & Astronomy; Jul2025, Vol. 68 Issue 7, p1-17, 17p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the advent of third-generation (3G) ground-based gravitational wave (GW) detectors, such as the Einstein Telescope (ET), we anticipate a substantial enhancement in sensitivity across a wide frequency range. The machine learning approach for GW search necessitates an update to address the challenges posed by data features that deviate from those of 2G detectors. In this paper, we introduce a novel GW search pipeline specifically designed for 3G ground-based detectors like ET. Our pipeline leverages three types of deep learning models: an envelope extraction model, a denoising model, and an astrophysical origin discrimination model. Additionally, we propose a signal consistency test across multiple detectors. Given that denoising results vary among different detectors, we present a new method for selecting the optimal waveform. This selected waveform serves as a template for estimating the signal-to-noise ratio (SNR) of strain data from all detectors. Furthermore, if 3G detectors operate alongside 2G detectors, the templates derived from 3G detector data can be utilized to predict the SNR for 2G detectors, significantly reducing the computational burden of GW searches for the latter. We also assess the robustness of our method when applied to data containing binary neutron star (BNS) foreground noise. We believe that the proposed method holds promise for detecting BBH events in future 3G detectors. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of SCIENCE CHINA Physics, Mechanics & Astronomy is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jul2025
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