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Direct test of the FLRW metric from strongly lensed gravitational wave observations
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The assumptions of large-scale homogeneity and isotropy underly the familiar Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) metric that appears to be an accurate description of our Universe. In this paper, we propose a new strategy of testing the validity of the FLRW metric, based on the galactic-scale lensing systems where strongly lensed gravitational waves and their electromagnetic counterparts can be simultaneously detected. Each strong lensing system creates opportunity to infer the curvature parameter of the Universe. Consequently, combined analysis of many such systems will provide a model-independent tool to test the validity of the FLRW metric. Our study demonstrates that the third-generation ground based GW detectors, like the Einstein Telescope (ET) and space-based detectors, like the Big Bang Observer (BBO), are promising concerning determination of the curvature parameter or possible detection of deviation from the FLRW metric. Such accurate measurements of the FLRW metric can become a milestone in precision GW cosmology.
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Cited by 3 Pith papers
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Identifying lensed gravitational waves with physics-informed posterior learning
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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Identifying multiple images of gravitational-wave sources lensed by elliptical lensing potentials
For singular isothermal ellipsoid lenses that produce four gravitational-wave images, three-image search templates reduce waveform mismatch by roughly a factor of two to five relative to two-image templates, and geome...
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Model independent test of the FLRW metric and the curvature in light of DESI DR2
An Ok-diagnostic analysis combining DESI DR2 BAO with Pantheon+ or DES Y5 supernovae yields curvature values consistent with a flat universe at the 2-4 sigma level, with the sign depending on the dataset.
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