Hierarchical Bayesian Detection Algorithm for Early-Universe Relics in the Cosmic Microwave Background
Stephen M. Feeney, Matthew C. Johnson, Jason D. McEwen, Daniel J., Mortlock, Hiranya V. Peiris

TL;DR
This paper develops a hierarchical Bayesian method and data analysis pipeline to detect early-universe relics like textures and bubble collisions in the CMB, applying it to WMAP data.
Contribution
It introduces an accurate approximation to the posterior distribution and a modular algorithm for robust detection and parameter estimation of localized sources in the CMB.
Findings
No evidence for textures or bubble collisions in WMAP data.
Constraints on the number of detectable sources: fewer than 4 bubble collisions and 5.2 textures at 95% confidence.
Enhanced detection sensitivity using adaptive-resolution techniques and optimal filters.
Abstract
A number of theoretically well-motivated additions to the standard cosmological model predict weak signatures in the form of spatially localized sources embedded in the cosmic microwave background (CMB) fluctuations. We present a hierarchical Bayesian statistical formalism and a complete data analysis pipeline for testing such scenarios. We derive an accurate approximation to the full posterior probability distribution over the parameters defining any theory that predicts sources embedded in the CMB, and perform an extensive set of tests in order to establish its validity. The approximation is implemented using a modular algorithm, designed to avoid a posteriori selection effects, which combines a candidate-detection stage with a full Bayesian model-selection and parameter-estimation analysis. We apply this pipeline to theories that predict cosmic textures and bubble collisions,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
