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An interference-based method for the detection of strongly lensed gravitational waves
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abstract
The strongly lensed gravitational wave (SLGW) is a promising transient phenomenon. However, the long-wave nature of gravitational waves poses a significant challenge in identification of its host galaxy. To tackle this challenge, we propose a method triggered by the wave optics effect of microlensing. The microlensing interference introduce frequency-dependent fluctuations in the waveform. Our method consists of three steps. First, we reconstruct the waveforms by using the template-independent and template-dependent methods. The mismatch of two reconstructions serves as an indicator of SLGWs. This step can identify approximately $10\%$ SLGWs. Second, we pair the SLGWs' multiple-images by employing the sky localization overlapping. Because we have pre-identified at least one image through microlensing, the false alarm probability for pairing SLGWs is significantly reduced. Third, we search the host galaxy by requiring the consistency of time-delays between galaxy-galaxy lensing and SLGW. By combing the stage-IV galaxy survey and the third-generation gravitational wave detectors, we expect to find, on average, 1 quadruple-image system per 3 years. The merit of this method can significantly facilitate the pursuit of time-delay cosmography, discovery of compact objects and multi-messenger astronomy.
Forward citations
Cited by 4 Pith papers
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Fusing a simulation-trained common-source mass posterior with waveform features raises lensed-event detection efficiency from 20.8% to 35.2% at 1% false-positive rate and lowers the SNR for 50% efficiency from 45.3 to 33.5.
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Can Eccentric Binary Black Hole Signals Mimic Gravitational-Wave Microlensing?
Eccentric BBH signals can masquerade as wave-optics microlensing in quasicircular analyses, but eccentric recovery templates break the degeneracy.
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Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning
A wavelet-convolution neural network distinguishes simulated lensed from unlensed gravitational waves with 92.2% accuracy (AUC 0.965) using wave-optics diffraction patterns.
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Spin Precession Signatures as an Indicator of Microlensing in Strongly Lensed Gravitational Waves
Microlensing wave-optics effects in strongly lensed gravitational waves can produce false evidence of spin precession, with the effect growing at higher signal-to-noise ratios.
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