Bayesian reconstruction and exhaustive symbolic regression on CMB, BAO, and supernova data yield the one-parameter dark energy parametrization w(a) = w0 / sqrt(a) that fits observations comparably to CPL and better than LambdaCDM.
Imprints of an extended Chevallier–Polarski–Linder parametrization on the large scale of our universe,
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Node-based reconstruction of cosmic expansion prefers stronger deceleration at z≈1.7 than smooth DE EoS parametrizations, isolating z~1.5-2 as a window where the latter may compress localized kinematic features permitted by current data.
Extended analysis of DESI DR2 data confirms robust evidence for dynamical dark energy with phantom crossing preference, stable under parametric and non-parametric modeling.
citing papers explorer
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Data-Driven Discovery of a Simple Phantom-Crossing Dark Energy Parametrization
Bayesian reconstruction and exhaustive symbolic regression on CMB, BAO, and supernova data yield the one-parameter dark energy parametrization w(a) = w0 / sqrt(a) that fits observations comparably to CPL and better than LambdaCDM.
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Do equation of state parametrizations of dark energy faithfully capture the dynamics of the late universe?
Node-based reconstruction of cosmic expansion prefers stronger deceleration at z≈1.7 than smooth DE EoS parametrizations, isolating z~1.5-2 as a window where the latter may compress localized kinematic features permitted by current data.
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Extended Dark Energy analysis using DESI DR2 BAO measurements
Extended analysis of DESI DR2 data confirms robust evidence for dynamical dark energy with phantom crossing preference, stable under parametric and non-parametric modeling.