Bayesian evidence for $\alpha$-attractor dark energy models

Dark energy models with tracker properties have gained attention due to the large range of initial conditions leading to the current value of the dark energy density parameter. A well-motivated family of these models are the so-called $\alpha$-attractors, which show the late time behavior of a cosmological constant. In the present paper we perform a model-selection analysis of a variety of $\alpha$-attractor potentials in comparison with a non-flat $\Lambda$CDM model. Specifically, we compute the Bayes Factor for the L-Model, the Oscillatory Tracker Model, the Recliner Model, and the Starobinsky Model, while considering the non-flat $\Lambda$CDM as the base model. Each model is tested through a Bayesian analysis using observations relevant to the current accelerated expansion: we employ the latest SNe Ia data, combined with cosmic clocks, the latest BOSS release of BAO data, and the Planck Compressed 2018 data. The produced Markov Chains for each model are further compared through a Bayesian evidence analysis. From the latter we conclude that the Oscillatory Tracker Model is preferred by data (even if weakly) over the non-flat $\Lambda$CDM model. Our results also suggest at the L-model is the least favoured version of the $\alpha$-attractor models considered.