Bayesian model selection on scalar <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>?</mml:mi></mml:math> -field dark energy

The main aim of this paper is to analyse minimally-coupled scalar-fields -- quintessence and phantom as the candidates explain accelerated expansion universe compare its observables current cosmological observations; a byproduct we present python module. This work includes parameter $\epsilon$ which allows incorporate both fields within same analysis. Examples potentials, so far included, are $V(\phi)=V_0\phi^{\mu}e^{\beta \phi^\alpha}$ $V(\phi)=V_0(\cosh(\alpha \phi)+\beta)$ with $\alpha$, $\mu$ $\beta$ being free parameters, but analysis can be easily extended any other scalar field potential. Additional component standard content matter, study also incorporates contribution from spatial curvature ($\Omega_k$), it has been focus in recent studies. contains most up-to-date datasets along nested sampler produce posterior distributions Bayesian evidence, that perform model selection. In constrain parameter-space describing two generic among several combinations, found best-fit given by slightly favouring potential $V(\phi)=V_0\phi^\mu e^{\beta \phi}$ $\beta=0.22\pm 1.56$, $\mu = -0.41\pm 1.90$, negative $\Omega_{k,0}=-0.0016\pm0.0018$, presents deviations $1.6\sigma$ $\Lambda$CDM model. Even though three extra evidence $\mathcal{B}_{\Lambda, \phi} =2.0$ unable distinguish compared ($\Omega_{k,0}=0.0013\pm0.0018$). provides minimal corresponds $V(\phi)=V_0 \cosh(\alpha \phi)$ $\alpha=-0.61\pm 1.36$.