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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K-inflation with non-canonical kinetic term G(φ) shifts α-attractor T-models and natural inflation into the Planck-ACT-LB-BK18 allowed region while satisfying Swampland conjectures and producing testable GW spectra.
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.
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.
citing papers explorer
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Implications of the LISA stochastic signal from eccentric stellar mass black hole binaries in vacuum
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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Reviving Motivated Inflationary Potentials with $K$-inflation in the light of ACT
K-inflation with non-canonical kinetic term G(φ) shifts α-attractor T-models and natural inflation into the Planck-ACT-LB-BK18 allowed region while satisfying Swampland conjectures and producing testable GW spectra.
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High-Quality Axion Dark Matter at Gravitational Wave Interferometers
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.
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Model Parameter Reconstruction of Electroweak Phase Transition with TianQin and LISA: Insights from the Dimension-Six Model
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.