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

TL;DR

This paper introduces a wavelet phase harmonics-based statistical denoising method for Planck dust polarization data, enabling better characterization of non-Gaussian dust emission features crucial for CMB studies.

Contribution

It presents a novel denoising technique that captures non-Gaussian statistics of dust emission from noisy observations, improving the modeling of interstellar dust polarization.

Findings

The denoising method accurately recovers the power spectrum at small scales.

It preserves non-Gaussian properties of the dust emission.

The approach is validated on simulated data and applied to Planck observations.

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.