In perturbatively moduli-stabilized brane-antibrane inflation, a modest fraction of annihilation energy into visible open strings can trigger a Hagedorn phase that suppresses ΔN_eff when the SM throat string scale is comparable or lower.
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Analytical approximations for the ultra-high-frequency GW peak sourced by friction-era cosmic string loops, with expanded observable parameter space.
Conformal U(1)' seesaw models produce PBHs contributing to dark matter and helical magnetic fields at seesaw scales of 10^4-10^11 GeV, with observable GW, microlensing, and Hawking signals at LISA, Roman, and future gamma-ray telescopes.
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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Fire at the Tip of the Throat: Hagedorn Phase after brane-antibrane inflation?
In perturbatively moduli-stabilized brane-antibrane inflation, a modest fraction of annihilation energy into visible open strings can trigger a Hagedorn phase that suppresses ΔN_eff when the SM throat string scale is comparable or lower.
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Gravitational waves from cosmic strings with friction: analytical approximations and parameter space
Analytical approximations for the ultra-high-frequency GW peak sourced by friction-era cosmic string loops, with expanded observable parameter space.
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Primordial black holes and magnetic fields in conformal neutrino mass models
Conformal U(1)' seesaw models produce PBHs contributing to dark matter and helical magnetic fields at seesaw scales of 10^4-10^11 GeV, with observable GW, microlensing, and Hawking signals at LISA, Roman, and future gamma-ray telescopes.
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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.