3D simulations in an expanding background show cosmic expansion drives nonlinear growth that amplifies gravitational-wave spectra from slow phase transitions by factors of 10 to 100.
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The end of thermal inflation proceeds by nucleating true-vacuum bubbles rather than by global phase mixing, and the resulting gravitational-wave background can reach BBO and DECIGO sensitivities for low flaton mass scale gamma.
Radiative electroweak symmetry breaking with a logarithmic potential yields analytical vacuum solutions, four thermal history patterns, and supercooled FOPT gravitational waves whose signals combined with collider data can probe conformal scales to 10^5-10^8 GeV.
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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Nonlinear growth and amplification of phase-transition gravitational waves induced by cosmic expansion
3D simulations in an expanding background show cosmic expansion drives nonlinear growth that amplifies gravitational-wave spectra from slow phase transitions by factors of 10 to 100.
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Phase Transitions and Gravitational Wave Production at the End of Thermal Inflation
The end of thermal inflation proceeds by nucleating true-vacuum bubbles rather than by global phase mixing, and the resulting gravitational-wave background can reach BBO and DECIGO sensitivities for low flaton mass scale gamma.
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Probing radiative electroweak symmetry breaking with colliders and gravitational waves
Radiative electroweak symmetry breaking with a logarithmic potential yields analytical vacuum solutions, four thermal history patterns, and supercooled FOPT gravitational waves whose signals combined with collider data can probe conformal scales to 10^5-10^8 GeV.
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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.