Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

The recent evidence for dynamical dark energy from DESI, in combination with other cosmological data, has generated significant interest in understanding the nature of dark energy and its underlying microphysics. However, interpreting these results critically depends on how dark energy is parameterized. This paper examines the robustness of conclusions about the viability of particular kinds of dynamical dark energy models to the choice of parameterization, focusing on four popular two-parameter families: the Chevallier-Polarski-Linder (CPL), Jassal-Bagla-Padmanabhan (JBP), Barboza-Alcaniz (BA), and exponential (EXP) parameterizations. We find that conclusions regarding the viability of minimally and non-minimally coupled quintessence models are independent of the parameterization adopted. We demonstrate this both by mapping these dark energy models into the (w 0, wa ) parameter space defined by these various parameterizations and by showing that all of these parameterizations can equivalently account for the phenomenology predicted by these dark energy models to a high degree of accuracy.

Abstract:

The Atacama Cosmology Telescope Data Release 6 (ACT DR6) power spectrum is expected to provide state-of-the-art cosmological constraints, with an associated need for precise error modeling. In this paper we design, and evaluate the performance of, an analytic covariance matrix prescription for the DR6 power spectrum that sufficiently accounts for the complicated ACT map properties. We use recent advances in the literature to handle sharp features in the signal and noise power spectra, and account for the effect of map-level anisotropies on the covariance matrix. In including inhomogeneous survey depth information, the resulting covariance matrix prescription is structurally similar to that used in the Planck Cosmic Microwave Background (CMB) analysis. We quantify the performance of our prescription using comparisons to Monte Carlo simulations, finding better than 3% agreement. This represents an improvement from a simpler, pre-existing prescription, which differs from simulations by ∼ 16%. We develop a new method to correct the analytic covariance matrix using simulations, after which both prescriptions achieve better than 1% agreement. This correction method outperforms a commonly used alternative, where the analytic correlation matrix is assumed to be accurate when correcting the covariance. Beyond its use for ACT, this framework should be applicable for future high resolution CMB experiments including the Simons Observatory (SO).