Defines peak-integrated sensitivity curves (PISCs) that fold in the expected spectral shape of gravitational waves from cosmological phase transitions and supplies semianalytical fits plus public data for major detectors.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
fields
hep-ph 4verdicts
UNVERDICTED 4roles
background 3polarities
background 3representative citing papers
In weak first-order phase transitions, subcritical bubbles reach percent-level volume fractions before critical nucleation when phases are nearly degenerate at Tn, invalidating the homogeneous-background assumption for points satisfying log10 f̂_ξ(Tn) ≃ -1.95.
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.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
citing papers explorer
-
New Sensitivity Curves for Gravitational-Wave Signals from Cosmological Phase Transitions
Defines peak-integrated sensitivity curves (PISCs) that fold in the expected spectral shape of gravitational waves from cosmological phase transitions and supplies semianalytical fits plus public data for major detectors.
-
Subcritical bubble prehistory in weak first-order phase transition
In weak first-order phase transitions, subcritical bubbles reach percent-level volume fractions before critical nucleation when phases are nearly degenerate at Tn, invalidating the homogeneous-background assumption for points satisfying log10 f̂_ξ(Tn) ≃ -1.95.
-
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.
-
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.