Optimal analysis of azimuthal features in the CMB

Understand

We present algorithms for searching for azimuthally symmetric features in CMB data.

• Our algorithms are fully optimal for masked all-sky data with inhomogeneous noise, computationally fast, simple to implement, and make no approximations.
• We show how to implement the optimal analysis in both Bayesian and frequentist cases.
• In the Bayesian case, our algorithm for evaluating the posterior likelihood is so fast that we can do a brute-force search over parameter space, rather than using a Monte Carlo Markov chain.