Frequency-domain simulations of the Taiji mission, including noise and foregrounds, demonstrate that the stochastic gravitational wave background from an electroweak phase transition can constrain Higgs cubic and quartic self-couplings in a singlet-extended Standard Model despite degeneracies.
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Sound shell collisions from Hubble-scale primordial density perturbations generate a stochastic GW background whose peak frequency and amplitude scale with the Hubble horizon and shell abundance.
Phase transitions in dark sectors can generate CMB B-modes with amplitudes competitive with inflation but peaking at smaller angular scales.
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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Measuring gravitational wave spectrum from electroweak phase transition and Higgs self-couplings
Frequency-domain simulations of the Taiji mission, including noise and foregrounds, demonstrate that the stochastic gravitational wave background from an electroweak phase transition can constrain Higgs cubic and quartic self-couplings in a singlet-extended Standard Model despite degeneracies.
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Relic gravitational waves from primordial gravitational collapses
Sound shell collisions from Hubble-scale primordial density perturbations generate a stochastic GW background whose peak frequency and amplitude scale with the Hubble horizon and shell abundance.
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Observable CMB B-modes from Cosmological Phase Transitions
Phase transitions in dark sectors can generate CMB B-modes with amplitudes competitive with inflation but peaking at smaller angular scales.
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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.