A new approach for the statistical denoising of Planck interstellar dust polarization data. (arXiv:2102.03160v2 [astro-ph.CO] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Blancard_B/0/1/0/all/0/1">Bruno Regaldo-Saint Blancard</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Allys_E/0/1/0/all/0/1">Erwan Allys</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Boulanger_F/0/1/0/all/0/1">François Boulanger</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Levrier_F/0/1/0/all/0/1">François Levrier</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Jeffrey_N/0/1/0/all/0/1">Niall Jeffrey</a>
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.
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.
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