Using SPTpol, Planck 2015, and non-CMB data to constrain tilted spatially-flat and untilted non-flat $\Lambda$CDM, XCDM, and $\phi$CDM dark energy inflation cosmologies

TL;DR

This study combines SPTpol, Planck 2015, and non-CMB data to analyze various dark energy cosmologies, finding that non-flat models are favored and non-CMB data significantly improve constraints, despite some fit discrepancies.

Contribution

It provides a comprehensive analysis of tilted flat and untilted non-flat dark energy models using multiple datasets, highlighting the constraining power of non-CMB data and the preference for spatially-closed models.

Findings

Non-flat models are favored over flat models when combined with Planck data.

Non-CMB data, especially BAO measurements, significantly tighten parameter constraints.

SPTpol data alone do not fit the models as well as Planck and non-CMB data.

Abstract

We use six tilted spatially-flat and untilted non-flat dark energy cosmological models in analyses of South Pole Telescope polarization (SPTpol) cosmic microwave background (CMB) data, alone and in combination with Planck 2015 CMB data and non-CMB data, namely, the Pantheon Type Ia supernovae apparent magnitudes, a collection of baryon acoustic oscillation data points, Hubble parameter measurements, and growth rates. Although the cosmological models that best-fit the Planck CMB and non-CMB data do not provide good fits to the SPTpol data, with the $\chi^2$'s exceeding the expected value, given the uncertainties, in each model the cosmological parameter constraints from the SPTpol data and from the Planck CMB and non-CMB data are largely mutually consistent. When the smaller angular scale SPTpol data are used jointly with either the Planck data alone or with the Planck CMB and the non-CMB data to constrain untilted non-flat models, spatially-closed models remain favored over their corresponding flat limits. When used in conjunction with Planck data, non-CMB data (baryon acoustic oscillation measurements in particular, from six experiments) have significantly more constraining power than the SPTpol data.