Hierarchical Bayesian inference on 20 high-SNR simulated binary neutron star events shows a linear lnΛ-lnQ relation suffices and constrains dynamical Chern-Simons gravity length scale to ≤10 km.
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Hybrid string-bounded domain wall networks from sequential U(1)_F and Z2 symmetry breaking generate a GW spectrum with a unique low-frequency slope that future detectors can observe and an MLP surrogate can characterize for fast SNR inference.
Reanalysis finds GW190521 prefers hyperbolic waveforms over quasi-circular precessing ones with ln Bayes factor 3.71, while other high-mass events and GW231123 favor the latter; mock signals indicate distinguishability challenges for high-mass precessing cases.
citing papers explorer
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Inferring neutron-star Love-Q relations from gravitational waves in the hierarchical Bayesian framework
Hierarchical Bayesian inference on 20 high-SNR simulated binary neutron star events shows a linear lnΛ-lnQ relation suffices and constrains dynamical Chern-Simons gravity length scale to ≤10 km.
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Gravitational Waves from hybrid defects as probe of Flavor symmetry breaking: Machine-Learning Approach
Hybrid string-bounded domain wall networks from sequential U(1)_F and Z2 symmetry breaking generate a GW spectrum with a unique low-frequency slope that future detectors can observe and an MLP surrogate can characterize for fast SNR inference.
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Gravitational Wave Hyperbolic Catalog: Reanalyzing High-Mass Gravitational Wave Signals Using Hyperbolic Waveforms
Reanalysis finds GW190521 prefers hyperbolic waveforms over quasi-circular precessing ones with ln Bayes factor 3.71, while other high-mass events and GW231123 favor the latter; mock signals indicate distinguishability challenges for high-mass precessing cases.