<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>AB</mml:mtext> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mtext>MB</mml:mtext> </mml:mrow> </mml:math> : deep delensing assisted likelihood-free inference from CMB po
Abstract The existence of a cosmic background of primordial gravitational waves is a robust prediction of inflationary cosmology, but it has so far evaded discovery. The most promising avenue of its detection is via measurements of cosmic microwave background (CMB) B -polarization. However, this is not straightforward due to (a) the fact that CMB maps are distorted by gravitational lensing and (b) the high-dimensional nature of CMB data, which renders likelihood-based analysis methods computationally extremely expensive. In this paper, we introduce an efficient likelihood-free, end-to-end inference method to directly infer the posterior distribution of the tensor-to-scalar ratio r from lensed maps of the Stokes Q and U polarization parameters. Our method employs a generative model to delense the maps and utilizes the approximate Bayesian computation algorithm to sample r . We demonstrate that our method yields unbiased estimates of r with well-calibrated uncertainty quantification.
Authors
- Yanan Fan (ORCID: https://orcid.org/0000-0003-3783-9179)
- Píetro Lió (ORCID: https://orcid.org/0000-0002-0540-5053)
- Yu Guang Wang (ORCID: https://orcid.org/0000-0002-7450-0273)
- Kai Yi (ORCID: https://orcid.org/0000-0002-6091-3122)
- Jan Hamann
Institutions
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Shanghai Jiao Tong University (CN)
- University of Cambridge (GB)
- UNSW Sydney (AU)
- Data61 (AU)
Publication Details
- Journal
- Machine Learning Science and Technology
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1088/2632-2153/ad9af9
- Primary Topic
- Gaussian Processes and Bayesian Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00