Non-parametric reconstruction of non-minimally coupled gravity with a smoothness prior on CMB, DESI BAO, supernovae, and DES data yields a 2.8σ hint for coupling and a preference for phantom divide crossing stabilized by the coupling.
Dark-Energy Evolution Across the Cosmological-Constant Boundary
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abstract
We explore the properties of dark energy models for which the equation-of-state, w, defined as the ratio of pressure to energy density, crosses the cosmological-constant boundary w = -1. We adopt an empirical approach, treating the dark energy as an uncoupled fluid or a generalized scalar field. We describe the requirements for a viable model, in terms of the equation-of-state and sound speed. A generalized scalar field cannot safely traverse w = -1, although a pair of scalars with w > -1 and w < -1 will work. A fluid description with a well-defined sound speed can also cross the boundary. Contrary to expectations, such a crossing model does not instantaneously resemble a cosmological constant at the moment w = -1 since the density and pressure perturbations do not necessarily vanish. But because a dark energy with w < -1 dominates only at very late times, and because the dark energy is not generally prone to gravitational clustering, then crossing the cosmological-constant boundary leaves no distinct imprint.
fields
astro-ph.CO 2representative citing papers
Extended analysis of DESI DR2 data confirms robust evidence for dynamical dark energy with phantom crossing preference, stable under parametric and non-parametric modeling.
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Late-time reconstruction of non-minimally coupled gravity with a smoothness prior
Non-parametric reconstruction of non-minimally coupled gravity with a smoothness prior on CMB, DESI BAO, supernovae, and DES data yields a 2.8σ hint for coupling and a preference for phantom divide crossing stabilized by the coupling.
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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.