AB$\mathbb{C}$MB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914869700198400 |
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| author | Yi, Kai Fan, Yanan Hamann, Jan Liò, Pietro Wang, Yuguang |
| author_facet | Yi, Kai Fan, Yanan Hamann, Jan Liò, Pietro Wang, Yuguang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AB$\mathbb{C}$MB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps Yi, Kai Fan, Yanan Hamann, Jan Liò, Pietro Wang, Yuguang Cosmology and Nongalactic Astrophysics Applications 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. |
| title | AB$\mathbb{C}$MB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps |
| topic | Cosmology and Nongalactic Astrophysics Applications |
| url | https://arxiv.org/abs/2407.10013 |