CosmoCoffee

This paper looks at how well Planck can do at distinguishing n_s \ne 1 models from n_s=1.

Why are the authors using the Gaussian likelihood in Eq. 1 rather than the true likelihood function? It is well known that the Gaussian approximation is not very good, and the true function is just as easy to compute with an effective full sky. (it's given explicitly in e.g. Eq 5 of astro-ph/0502469, though I certainly wasn't the first to write it down). Do you get out the correct posterior constraints using this function?

Hi Antony,

We used Gaussian likelihoods, for no reason other than that we thought that the non-Gaussianity of the likelihood functions for C_2 - C_10 will not make a significant difference. But you're right, it is as easy to use the true likelihood function. We'll re-run the analysis through and get back. Our guess is that the result wont change much (as the posteriors look alright), but we'll see.

We have recomputed everything using the improved likelihood function. The updated paper is on astro-ph today. The numbers changed a little, the paper/results are qualitatively unchanged.