Single frequency CMB B-mode inference with realistic foregrounds from a single training image
| Autor: | Jeffrey, Niall, Boulanger, François, Wandelt, Benjamin D., Blancard, Bruno Regaldo-Saint, Allys, Erwan, Levrier, François |
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| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1093/mnrasl/slab120 |
| Popis: | With a single training image and using wavelet phase harmonic augmentation, we present polarized Cosmic Microwave Background (CMB) foreground marginalization in a high-dimensional likelihood-free (Bayesian) framework. We demonstrate robust foreground removal using only a single frequency of simulated data for a BICEP-like sky patch. Using Moment Networks we estimate the pixel-level posterior probability for the underlying {E,B} signal and validate the statistical model with a quantile-type test using the estimated marginal posterior moments. The Moment Networks use a hierarchy of U-Net convolutional neural networks. This work validates such an approach in the most difficult limiting case: pixel-level, noise-free, highly non-Gaussian dust foregrounds with a single training image at a single frequency. For a real CMB experiment, a small number of representative sky patches would provide the training data required for full cosmological inference. These results enable robust likelihood-free, simulation-based parameter and model inference for primordial B-mode detection using observed CMB polarization data. Comment: Accepted by Monthly Notices of the Royal Astronomical Society Letters. 5 pages with 3 figures (plus 1 page of Supporting Materials with 2 figures) |
| Databáze: | arXiv |
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