Continuous spin gravity yields GW time-delay deviations of order ρ_g/ω from GR predictions for frequencies above ρ_g, with damping below, enabling sensitivity to spin scales ~10^{-14} eV at ground interferometers and ~10^{-24} eV at PTAs.
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Deconvolution of the GWTC-4.0 BBH merger rate reveals that long-delay tails in the delay time distribution are forbidden, constraining progenitor formation histories to decline more steeply than the star formation rate and disfavoring shallow power-law DTDs such as stable mass transfer.
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
citing papers explorer
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First look at continuous spin gravity: Time delay signatures
Continuous spin gravity yields GW time-delay deviations of order ρ_g/ω from GR predictions for frequencies above ρ_g, with damping below, enabling sensitivity to spin scales ~10^{-14} eV at ground interferometers and ~10^{-24} eV at PTAs.
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Forbidden Formation Histories: The Binary Black Hole Merger Rate Disfavors Long Delay Times
Deconvolution of the GWTC-4.0 BBH merger rate reveals that long-delay tails in the delay time distribution are forbidden, constraining progenitor formation histories to decline more steeply than the star formation rate and disfavoring shallow power-law DTDs such as stable mass transfer.
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Reconstruction of fast-rotating neutron star observables with the neural network
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.