A new approach for the statistical denoising of Planck interstellar dust polarization data

Abstract

Dust emission is the main foreground for cosmic microwave background (CMB) polarization. Its statistical characterization must be derived from the analysis of observational data because the precision required for a reliable component separation is far greater than what is currently achievable with physical models of the turbulent magnetized interstellar medium. This letter takes a significant step toward this goal by proposing a method that retrieves non-Gaussian statistical characteristics of dust emission from noisy Planck polarization observations at 353 GHz. We devised a statistical denoising method based on wavelet phase harmonics (WPH) statistics, which characterize the coherent structures in non-Gaussian random fields and define a generative model of the data. The method was validated on mock data combining a dust map from a magnetohydrodynamic simulation and Planck noise maps. The denoised map reproduces the true power spectrum down to scales where the noise power is an order of magnitude larger than that of the signal. It remains highly correlated to the true emission and retrieves some of its non-Gaussian properties. Applied to Planck data, the method provides a new approach to building a generative model of dust polarization that will characterize the full complexity of the dust emission. We also release PyWPH, a public Python package, to perform GPU-accelerated WPH analyses on images.

10 pages, 9 figures, accepted by A&A

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• Scattering Spectra Models for Physics
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• Generative Models of Multi-channel Data from a Single Example -- Application to Dust Emission
• Self-supervised component separation for the extragalactic submillimeter sky
• From few to many maps: A fast map-level emulator for extreme augmentation of CMB systematics datasets
• Quantitative Morphology of Galactic Cirrus in Deep Optical Imaging
• Constraining Power of Wavelet vs. Power Spectrum Statistics for CMB Lensing and Weak Lensing with Learned Binning
• Bayesian imaging inverse problem with scattering transform