An iterative AI reasoning process proposes new dynamical dark energy equations of state that are competitive with traditional forms on supernova, BAO, and Planck data.
WMAP constraints on low redshift evolution of dark energy
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abstract
The conceptual difficulties associated with a cosmological constant have led to the investigation of alternative models in which the equation of state parameter, $w=p/\rho$, of the dark energy evolves with time. We show that combining the supernova type Ia observations {\it with the constraints from WMAP observations} restricts large variation of $\rho(z)$ at low redshifts. The combination of these two observational constraints is stronger than either one. The results are completely consistent with the cosmological constant as the source of dark energy.
fields
astro-ph.CO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Physics-guided discovery of dynamical dark-energy equations of state through iterative AI reasoning
An iterative AI reasoning process proposes new dynamical dark energy equations of state that are competitive with traditional forms on supernova, BAO, and Planck data.
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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.