TL;DR
This paper introduces a Bayesian method for separating polarised CMB signals from Galactic foregrounds, accounting for spectral index variations and noise correlations, improving accuracy for future CMB experiments.
Contribution
The paper presents a novel Bayesian parametric component separation technique that models spectral index variations and noise correlations in polarised microwave sky maps.
Findings
Evidence for spatial variation of synchrotron spectral index
No evidence for dust depolarisation
Estimated spectral indices: beta-sync = -2.833, beta-dust = 1.429
Abstract
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…
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