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.
Dark energy equation of state parameter and its variation at low redshifts
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abstract
In this paper, we constrain dark energy models using a compendium of observations at low redshifts. We consider the dark energy as a barotropic fluid, with the equation of state a constant as well the case where dark energy equation of state is a function of time. The observations considered here are Supernova Type Ia data, Baryonic Acoustic Oscillation data and Hubble parameter measurements. We compare constraints obtained from these data and also do a combined analysis. The combined observational constraints put strong limits on variation of dark energy energy density with redshift. For varying dark energy models, the range of parameters preferred by the supernova type Ia data is in tension with the other low redshift distance measurements.
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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.
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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.