FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
2103.05408 , archiveprefix =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Non-parametric EOS construction from crust to pQCD forces a sound-speed peak and softening that realizes a generalized quark-hadron crossover in massive neutron stars.
With current multi-messenger EOS constraints, the post-merger peak frequency f2,mean is determined to ~100 Hz at fixed mass and tidal deformability/radius, tight enough to expose thermal or phase-transition physics.
PSR J0740+6620 has an equatorial radius of 13.7^{+2.6}_{-1.5} km, and multi-messenger data constrain 1.4 and 2.08 solar-mass neutron star radii to 12.45 and 12.35 km respectively.
citing papers explorer
-
Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
-
The Non-parametric Equation of State Realizes a Generalized Quark-Hadron Crossover
Non-parametric EOS construction from crust to pQCD forces a sound-speed peak and softening that realizes a generalized quark-hadron crossover in massive neutron stars.
-
Post-Merger Gravitational-Wave Uncertainties of Binary Neutron Stars under Multi-Messenger EOS Constraints
With current multi-messenger EOS constraints, the post-merger peak frequency f2,mean is determined to ~100 Hz at fixed mass and tidal deformability/radius, tight enough to expose thermal or phase-transition physics.
-
The Radius of PSR J0740+6620 from NICER and XMM-Newton Data
PSR J0740+6620 has an equatorial radius of 13.7^{+2.6}_{-1.5} km, and multi-messenger data constrain 1.4 and 2.08 solar-mass neutron star radii to 12.45 and 12.35 km respectively.