FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
2106.00428 , archiveprefix =
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A unified dark energy model with sigmoid correction generates a spectrum of rip futures that all fit DESI, Pantheon+, and CMB data at the same level as ΛCDM.
Generalized interacting dark energy models with constant or dynamical couplings yield analytical density expressions but are not preferred over LambdaCDM by Bayesian evidence from DESI, Pantheon+, and CMB data.
Modified IDE model with interaction parameter alpha ~0.01 from late-universe data shows H0 decreasing with redshift, tightening to 10^-5 when CMB priors are added.
citing papers explorer
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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.
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A Spectrum of Cosmological Rips and Their Observational Signatures
A unified dark energy model with sigmoid correction generates a spectrum of rip futures that all fit DESI, Pantheon+, and CMB data at the same level as ΛCDM.
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Generalizing the CPL Parametrization through Dark Sector Interaction
Generalized interacting dark energy models with constant or dynamical couplings yield analytical density expressions but are not preferred over LambdaCDM by Bayesian evidence from DESI, Pantheon+, and CMB data.
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Redshift evolution of the Hubble constant: Constraints and new insights from an interacting dark energy model
Modified IDE model with interaction parameter alpha ~0.01 from late-universe data shows H0 decreasing with redshift, tightening to 10^-5 when CMB priors are added.