<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.

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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
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<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

Yanan Fan, Píetro Lió, Yu Guang Wang, Kai Yi et al.
Machine Learning Science and Technology
Gaussian Processes and Bayesian Inference
article

<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

Yanan Fan, Píetro Lió, Yu Guang Wang, Kai Yi, Jan Hamann
article en

Abstract

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.

Machine Learning Science and TechnologyVol. 5(4)
Commonwealth Scientific and Industrial Research Organisation (AU), Shanghai Jiao Tong University (CN), University of Cambridge (GB), UNSW Sydney (AU), Data61 (AU)
Decent work and economic growth
Openalex Percentile: Top 80%
Gaussian Processes and Bayesian Inference
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<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 — Yanan Fan, Píetro Lió, et al. · Machine Learning Science and Technology (2024) | TGRS Research Map | TGRS