Kavli Affiliate: George Efstathiou
| First 5 Authors: Roger de Belsunce, Steven Gratton, George Efstathiou, ,
| Summary:
We present a Bayesian parametric component separation method for polarised
microwave sky maps. We solve jointly for the primary cosmic microwave
background (CMB) signal and the main Galactic polarised foreground components.
For the latter, we consider electron-synchrotron radiation and thermal dust
emission, modelled in frequency as a power law and a modified blackbody
respectively. We account for inter-pixel correlations in the noise covariance
matrices of the input maps and introduce a spatial correlation length in the
prior matrices for the spectral indices beta. We apply our method to
low-resolution polarised Planck 2018 Low and High Frequency Instrument
(LFI/HFI) data, including the SRoll2 re-processing of HFI data. We find
evidence for spatial variation of the synchrotron spectral index, and no
evidence for depolarisation of dust. Using the HFI SRoll2 maps, and applying
wide priors on the spectral indices, we find a mean polarised synchrotron
spectral index over the unmasked sky of beta-sync = -2.833 +- 0.620. For
polarised dust emission, we obtain beta-dust = 1.429 +- 0.236. Our method
returns correlated uncertainties for all components of the sky model. Using our
recovered CMB maps and associated uncertainties, we constrain the optical depth
to reionization, tau, using a cross-spectrum-based likelihood-approximation
scheme (momento) to be tau = 0.0598 +- 0.0059. We confirm our findings using a
pixel-based likelihood (pixlike). In both cases, we obtain a result that is
consistent with, albeit a fraction of a sigma higher than, that found by
subtracting spatially uniform foreground templates. While the latter method is
sufficient for current polarisation data from Planck, next-generation
space-borne CMB experiments will need more powerful schemes such as the one
presented here.
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