AB$\mathbb{C}$MB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps

Document detail

ID

oai:arXiv.org:2407.10013

Topic

Astrophysics - Cosmology and Nonga... Statistics - Applications

Author

Yi, Kai Fan, Yanan Hamann, Jan Liò, Pietro Wang, Yuguang

Year

2024

listing date

7/17/2024

Keywords

$r$ cmb

Metrics

Abstract

The existence of a cosmic background of primordial gravitational waves (PGWB) 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 (ABC) algorithm to sample $r$.

We demonstrate that our method yields unbiased estimates of $r$ with well-calibrated uncertainty quantification.

Yi, Kai,Fan, Yanan,Hamann, Jan,Liò, Pietro,Wang, Yuguang, 2024, AB$\mathbb{C}$MB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps