Treffer: DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data

DESI Collaboration: K. Lodha et al. -- arXiv:2405.13588v2 ; Baryon acoustic oscillation data from the first year of the Dark Energy Spectroscopic Instrument (DESI) provide near percent-level precision of cosmic distances in seven bins over the redshift range z=0.1-4.2. This paper is the follow-up to the original DESI BAO cosmology paper [A. G. Adame (DESI Collaboration), arXiv:2404.03002], which considered the conventional w0wa cold dark matter (CDM) model. We use the novel DESI data, together with other cosmic probes, to constrain the background expansion history using some well-motivated physical classes of dark energy. In particular, we explore three physics-focused behaviors of dark energy from the equation of state and energy density perspectives: the thawing class (matching many simple quintessence potentials), emergent class (where dark energy comes into being recently, as in phase transition models), and mirage class [where phenomenologically the distance to cosmic microwave background (CMB) last scattering is close to that from a cosmological constant Λ despite dark energy dynamics]. All three classes fit the data at least as well as ΛCDM, and indeed can improve on it by Δχ2≈-5 to -17 for the combination of DESI BAO with CMB and supernova data while having one more parameter. The mirage class does essentially as well as w0waCDM and exhibits moderate to strong Bayesian evidence preference with respect to ΛCDM. These classes of dynamical behaviors highlight worthwhile avenues for further exploration into the nature of dark energy. ; The authors thank Luis Urena-Lopez for his help with the polychord runs. We acknowledge the use of the Boltzmann solver class [49,50] for the computation of theoretical observables, cobaya [42] for the sampling and getdist [64] for the post-processing of our results. We also acknowledge the use of the standard python libraries for scientific computing, such as numpy [65], scipy [66] and matplotlib [67]. This work was supported by the high-performance computing cluster Seondeok ...