Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
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Dynamical dark energy remains preferred across extended models while curvature, neutrino mass and inflation parameters show strong model dependence, with no resolution of the H0 tension.
Phase-space analysis of a quadratic dark energy model identifies stable attractors that approach the phantom divide asymptotically from both sides without crossing it.
Re-analysis with PR4 Planck likelihoods reduces lensing anomaly significance and curvature preference in Lambda CDM extensions while indicating a preference for evolving dark energy consistent with DESI.
citing papers explorer
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Model-independent calibration of Gamma-Ray Bursts with neural networks
Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
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Intertwined Constraints in Extended Cosmologies: Dark Energy, Curvature, Neutrinos, and Inflation
Dynamical dark energy remains preferred across extended models while curvature, neutrino mass and inflation parameters show strong model dependence, with no resolution of the H0 tension.
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Phase-Space Analysis of Quadratic Dark Energy: Stable Attractors and Their Observational Signatures
Phase-space analysis of a quadratic dark energy model identifies stable attractors that approach the phantom divide asymptotically from both sides without crossing it.
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Revisiting $\Lambda$CDM extensions in light of re-analyzed CMB data
Re-analysis with PR4 Planck likelihoods reduces lensing anomaly significance and curvature preference in Lambda CDM extensions while indicating a preference for evolving dark energy consistent with DESI.
- Exploring the interplay of late-time dynamical dark energy and new physics before recombination