A first-principles perturbative framework is developed to constrain the Moon's elastic parameters and density structure from seismic responses to calibrated gravitational waves, claiming an order-of-magnitude error reduction.
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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.
LGWA could observe more than one third of known binary black hole events, detect ~90 mergers per year, and measure chirp mass better than third-generation detectors for massive systems.
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.
citing papers explorer
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Gravitational-wave Tomography of the Moon: Constraining Lunar Structure with Calibrated Gravitational Waves
A first-principles perturbative framework is developed to constrain the Moon's elastic parameters and density structure from seismic responses to calibrated gravitational waves, claiming an order-of-magnitude error reduction.
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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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Gravitational-wave parameter estimation to the Moon and back: massive binaries and the case of GW231123
LGWA could observe more than one third of known binary black hole events, detect ~90 mergers per year, and measure chirp mass better than third-generation detectors for massive systems.
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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.