High initial eccentricities in stellar-mass black hole binaries produce a stochastic gravitational wave background distinguishable by LISA from quasi-circular models, enabling upper bounds on eccentricity and separation of environmental effects for dense gas.
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A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.
Presents an efficient time-frequency Bayesian gap-filling technique that avoids costly matrix operations and integrates into LISA Global Fit, demonstrated on simulated LISA data.
Models directional galactic GW background with Doppler effects from Milky Way kinematics and shows via Fisher forecast that neglecting rotation produces observable biases in LISA parameter inference.
In gauged U(1) completions enabling high-quality axion dark matter, cosmic string loops generate a stochastic gravitational wave background with an infrared break frequency that exceeds foregrounds above 10^14 GeV breaking scales and offers a probe at interferometers.
Population properties of resolved galactic binaries can be used to model and subtract the confusion foreground, yielding feasible detection of stochastic gravitational wave backgrounds in Taiji simulations under statistical assumptions.
Simulations show TianQin and LISA can reconstruct the dimension-six model parameter Λ to sub-percent statistical precision for strong signals using Fisher, Bayesian sampling, and machine learning on data with noise and foregrounds.
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The Doppler effect of the Milky Way rotation on LISA
Models directional galactic GW background with Doppler effects from Milky Way kinematics and shows via Fisher forecast that neglecting rotation produces observable biases in LISA parameter inference.