Dr. Tassia Ferreira

Abstract:

Covariance matrices are among the most difficult pieces of end-to-end cosmological analyses. In principle, for two-point functions, each component involves a four-point function, and the resulting covariance often has hundreds of thousands of elements. We investigate various compression mechanisms capable of vastly reducing the size of the covariance matrix in the context of cosmic shear statistics. This helps identify which of its parts are most crucial to parameter estimation. We start with simple compression methods, by isolating and “removing” 200 modes associated with the lowest eigenvalues, then those with the lowest signal-to-noise ratio, before moving on to more sophisticated schemes like compression at the tomographic level and, finally, with the massively optimized parameter estimation and data compression (MOPED). We find that, while most of these approaches prove useful for a few parameters of interest, like Ωm, the simplest yield a loss of constraining power on the intrinsic alignment (IA) parameters as well as S8. For the case considered—cosmic shear from the first year of data from the Dark Energy Survey—only MOPED was able to replicate the original constraints in the 16-parameter space. Finally, we apply a tolerance test to the elements of the compressed covariance matrix obtained with MOPED and confirm that the IA parameter AIA is the most susceptible to inaccuracies in the covariance matrix.

Abstract:

We perform a Bayesian model selection analysis for different classes of phenomenological coupled scenarios of dark matter and dark energy with linear and non-linear interacting terms. We use a combination of some of the latest cosmological data such as type Ia supernovae (SNe Ia), cosmic chronometers (CC), cosmic microwave background (CMB) and two sets of baryon acoustic oscillations measurements, namely, 2-dimensional angular measurements (BAO2) and 3-dimensional angle-averaged measurements (BAO3). We find weak and moderate evidence against two-thirds of the interacting scenarios considered with respect to ΛCDM when the full joint analysis is considered. About one-third of the models provide a description to the data as good as the one provided by the standard model. Our results also indicate that either SNe Ia, CC or BAO2 data by themselves are not able to distinguish among interacting models or ΛCDM but the standard BAO3 measurements and the combination with the CMB data are indeed able to discriminate among them. We find that evidence disfavoring interacting models is weaker when we use BAO2 (data claimed to be almost model-independent) instead of the standard BAO3 measurements. These results help select classes of viable and non-viable interacting models in light of current data.