Bayesian modeling of microwave foregrounds

Abstract

In the past decade, advances in precision cosmology have pushed our understanding of the evolving Universe to new limits. Since the discovery of the cosmic microwave background (CMB) radiation in 1965 by Penzias and Wilson, precise measurements of various cosmological parameters have provided a glimpse into the dynamics of the early Universe and the fate that awaits it in the very distant future. However, these measurements are hindered by the presence of strong foreground contamination (synchrotron, free-free, dust emission) from the interstellar medium in our own Galaxy and others that masks the CMB signal. Recent developments in modeling techniques may provide a better understanding of these foregrounds and allow improved constraints on current cosmological models. The method of nested sampling [16, 5], a Bayesian inference technique for calculating the evidence (the average of the likelihood over the prior mass), promises to be efficient and accurate for modeling the microwave foregrounds masking the CMB signal. An efficient and accurate algorithm would prove extremely useful for analyzing data obtained from current and future CMB experiments. This analysis aims to characterize the behavior of the nested sampling algorithm. We create a physically realistic data simulation, which we then use to reconstruct the CMB sky using both the Internal Linear Combination (ILC) method and nested sampling. The accuracy of the reconstruction is determined by figures of merit based on the RMS of the reconstruction, residuals and foregrounds. We find that modeling the foregrounds by nested sampling produces the most accurate results when the spectral index for the dust foreground component is fixed.

(cont.) Although the reconstructed foregrounds are qualitatively similar to what is expected, none of the non-linear models produce a CMB map as accurate as that produced by internal linear combination(ILC). More over, additional low-frequency components (synchrotron steepening, spinning dust) produce inconclusive results. Further study is needed to improve efficiency and accuracy of the nested sampling algorithm.