Simulations for Einstein Telescope and Cosmic Explorer forecast that decade-scale samples of strongly lensed GWs can constrain the cosmic dipole magnitude at the few×10^{-3} level when combined with multi-image events.
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Combining GWTC-4 standard sirens with TDCOSMO2025 lensing data under the distance sum rule yields H0 = 83.78 +12.53/-10.23 km/s/Mpc (13.6% precision) in one configuration, consistent with both Planck and SH0ES.
Forecasts that cross-correlating 3G GW dark sirens with CSST photometric galaxies yields 1.04% precision on H0 and 2.04% on Omega_m while also constraining GW clustering bias.
Cross-correlating CSST galaxies with mock GW catalogs from ET2CE and BDET2CE networks can detect PBH merger fractions above ~40% and ~20% respectively via clustering bias differences.
citing papers explorer
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Prospect of Measuring the Cosmic Dipole by Associating Strongly Lensed Gravitational Waves with Galaxy Surveys
Simulations for Einstein Telescope and Cosmic Explorer forecast that decade-scale samples of strongly lensed GWs can constrain the cosmic dipole magnitude at the few×10^{-3} level when combined with multi-image events.
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Model-independent H0 from GWTC-4 standard sirens and TDCOSMO 2025 strong lensing time delays
Combining GWTC-4 standard sirens with TDCOSMO2025 lensing data under the distance sum rule yields H0 = 83.78 +12.53/-10.23 km/s/Mpc (13.6% precision) in one configuration, consistent with both Planck and SH0ES.
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Synergy between CSST and third-generation gravitational-wave detectors: Inferring cosmological parameters using cross-correlation of dark sirens and galaxies
Forecasts that cross-correlating 3G GW dark sirens with CSST photometric galaxies yields 1.04% precision on H0 and 2.04% on Omega_m while also constraining GW clustering bias.
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Synergy between CSST and future gravitational-wave detectors: Probing primordial black holes by cross-correlating dark sirens with galaxies
Cross-correlating CSST galaxies with mock GW catalogs from ET2CE and BDET2CE networks can detect PBH merger fractions above ~40% and ~20% respectively via clustering bias differences.