Probing local non-Gaussianities within a Bayesian framework

Aims. We outline the Bayesian approach to inferring f(NL), the level of non-Gaussianities of local type. Phrasing f(NL) inference in a Bayesian framework takes advantage of existing techniques to account for instrumental effects and foreground contamination in CMB data and takes into account uncertainties in the cosmological parameters in an unambiguous way. Methods. We derive closed form expressions for the joint posterior of f(NL) and the reconstructed underlying curvature perturbation, Phi, and deduce the conditional probability densities for f(NL) and Phi. Completing the inference problem amounts to finding the marginal density for f(NL). For realistic data sets the necessary integrations are intractable. We propose an exact Hamiltonian sampling algorithm to generate correlated samples from the f(NL) posterior. For sufficiently high signal-to-noise ratios, we can exploit the assumption of weak non-Gaussianity to find a direct Monte Carlo technique to generate independent samples from the posterior distribution for f(NL). We illustrate our approach using a simplified toy model of CMB data for the simple case of a 1D sky. Results. When applied to our toy problem, we find that, in the limit of high signal-to-noise, the sampling efficiency of the approximate algorithm outperforms that of Hamiltonian sampling by two orders of magnitude. When f(NL) is not significantly constrained by the data, the more efficient, approximate algorithm biases the posterior density towards f(NL) = 0.