Optimal analysis of azimuthal features in the CMB

TL;DR

This paper introduces optimal, fast algorithms for detecting azimuthally symmetric features in CMB data, capable of handling masked all-sky observations with inhomogeneous noise, applicable to signals like bubble collisions from early universe models.

Contribution

The paper develops a fully optimal analysis method for azimuthal features in CMB data, usable in Bayesian and frequentist frameworks, with efficient likelihood evaluation and broad applicability.

Findings

Algorithms are computationally fast and simple to implement.

The Bayesian likelihood evaluation allows brute-force parameter space search.

Applicable to detecting bubble collisions and other azimuthal features in CMB data.

Abstract

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. Our motivating example is searching for bubble collisions, a pre-inflationary signal which can be generated if multiple tunneling events occur in an eternally inflating spacetime, but our algorithms are general and should be useful in other contexts.