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Detection and Removal of B-mode Dust Foregrounds with Signatures of Statistical Anisotropy
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Detection and Removal of B-mode Dust Foregrounds with Signatures of Statistical Anisotropy
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Searches for inflationary gravitational wave signals in the CMB B-mode polarisation are expected to reach unprecedented power over the next decade. A major difficulty in these ongoing searches is that galactic foregrounds such as dust can easily mimic inflationary signals. Though typically foregrounds are separated from primordial signals using the foregrounds' different frequency dependence, in this paper we investigate instead the extent to which the galactic dust B-modes' statistical anisotropy can be used to distinguish them from inflationary B-modes, building on the work of Kamionkowski and Kovetz (2014). In our work, we extend existing anisotropy estimators and apply them to simulations of polarised dust to forecast their performance for future experiments. Considering the application of this method as a null-test for dust contamination to CMB-S4, we find that we can detect residual dust levels corresponding to $r\sim0.001$ at $2\sigma$, which implies that statistical anisotropy estimators will be a powerful diagnostic for foreground residuals (though our results show some dependence on the dust simulation used). Finally, considering applications beyond a simple null test, we demonstrate how anisotropy statistics can be used to construct an estimate of the dust B-mode map, which could potentially be used to clean the B-mode sky.
Forward citations
Cited by 1 Pith paper
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Single Frequency CMB Foreground Removal with Inter-scale Machine Learning
A hybrid CNN using both inter-scale and multi-frequency dust correlations achieves residual B-mode foreground power 3.62e-4 in DustFilaments simulations, about 7x lower than spatial ILC.
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