A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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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.
Simulations indicate joint Taiji+LISA analysis of five SLGW events yields H0 95% credible interval uncertainties of 0.11 (source redshift unknown) or 0.042 (source redshift known).
Gaussian Process Regression on mock GW siren catalogues reconstructs comoving distance and derivatives, showing that derivative diagnostics at specific redshifts best separate cosmological models while background data alone does not.
citing papers explorer
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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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.
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Measuring the Hubble constant with strongly lensed gravitational waves from space-based detector networks
Simulations indicate joint Taiji+LISA analysis of five SLGW events yields H0 95% credible interval uncertainties of 0.11 (source redshift unknown) or 0.042 (source redshift known).
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Gaussian Process Reconstruction of Cosmological Parameters with Gravitational Wave Sirens using Machine Learning
Gaussian Process Regression on mock GW siren catalogues reconstructs comoving distance and derivatives, showing that derivative diagnostics at specific redshifts best separate cosmological models while background data alone does not.