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Register a new userA new approach for the statistical denoising of Planck interstellar dust polarization data
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Bruno Regaldo-Saint Blancard
Publication date
2021
fields
Physics
and research's language is
English
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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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The statistical characterization of the diffuse magnetized ISM and Galactic foregrounds to the CMB poses a major challenge. To account for their non-Gaussian statistics, we need a data analysis approach capable of efficiently quantifying statistical couplings across scales. This information is encoded in the data, but most of it is lost when using conventional tools, such as one-point statistics and power spectra. The wavelet scattering transform (WST), a low-variance statistical descriptor of non-Gaussian processes introduced in data science, opens a path towards this goal. We applied the WST to noise-free maps of dust polarized thermal emission computed from a numerical simulation of MHD turbulence. We analyzed normalized complex Stokes maps and maps of the polarization fraction and polarization angle. The WST yields a few thousand coefficients; some of them measure the amplitude of the signal at a given scale, and the others characterize the couplings between scales and orientations. The dependence on orientation can be fitted with the reduced WST (RWST), an angular model introduced in previous works. The RWST provides a statistical description of the polarization maps, quantifying their multiscale properties in terms of isotropic and anisotropic contributions. It allowed us to exhibit the dependence of the map structure on the orientation of the mean magnetic field and to quantify the non-Gaussianity of the data. We also used RWST coefficients, complemented by additional constraints, to generate random synthetic maps with similar statistics. Their agreement with the original maps demonstrates the comprehensiveness of the statistical description provided by the RWST. This work is a step forward in the analysis of observational data and the modeling of CMB foregrounds. We also release PyWST, a Python package to perform WST/RWST analyses at: https://github.com/bregaldo/pywst.
The characterization of the dust polarization foreground to the Cosmic Microwave Background (CMB) is a necessary step towards the detection of the B-mode signal associated with primordial gravitational waves. We present a method to simulate maps of polarized dust emission on the sphere, similarly to what is done for the CMB anisotropies. This method builds on the understanding of Galactic polarization stemming from the analysis of Planck data. It relates the dust polarization sky to the structure of the Galactic magnetic field and its coupling with interstellar matter and turbulence. The Galactic magnetic field is modelled as a superposition of a mean uniform field and a random component with a power-law power spectrum of exponent $alpha_{rm M}$. The model parameters are constrained to fit the power spectra of dust polarization EE, BB and TE measured using Planck data. We find that the slopes of the E and B power spectra of dust polarization are matched for $alpha_{rm M} = -2.5$. The model allows us to compute multiple realizations of the Stokes Q and U maps for different realizations of the random component of the magnetic field, and to quantify the variance of dust polarization spectra for any given sky area outside of the Galactic plane. The simulations reproduce the scaling relation between the dust polarization power and the mean total dust intensity including the observed dispersion around the mean relation. We also propose a method to carry out multi-frequency simulations including the decorrelation measured recently by Planck, using a given covariance matrix of the polarization maps. These simulations are well suited to optimize component separation methods and to quantify the confidence with which the dust and CMB B-modes can be separated in present and future experiments. We also provide an astrophysical perspective on our modeling of the dust polarization spectra.
We describe the processing of the 336 billion raw data samples from the High Frequency Instrument (HFI) which we performed to produce six temperature maps from the first 295 days of Planck-HFI survey data. These maps provide an accurate rendition of the sky emission at 100, 143, 217, 353, 545 and 857 GHz with an angular resolution ranging from 9.9 to 4.4^2. The white noise level is around 1.5 {mu}K degree or less in the 3 main CMB channels (100--217GHz). The photometric accuracy is better than 2% at frequencies between 100 and 353 GHz and around 7% at the two highest frequencies. The maps created by the HFI Data Processing Centre reach our goals in terms of sensitivity, resolution, and photometric accuracy. They are already sufficiently accurate and well-characterised to allow scientific analyses which are presented in an accompanying series of early papers. At this stage, HFI data appears to be of high quality and we expect that with further refinements of the data processing we should be able to achieve, or exceed, the science goals of the Planck project.
Using the Planck 2015 data release (PR2) temperature maps, we separate Galactic thermal dust emission from cosmic infrared background (CIB) anisotropies. For this purpose, we implement a specifically tailored component-separation method, the so-called generalized needlet internal linear combination (GNILC) method, which uses spatial information (the angular power spectra) to disentangle the Galactic dust emission and CIB anisotropies. We produce significantly improved all-sky maps of Planck thermal dust emission, with reduced CIB contamination, at 353, 545, and 857 GHz. By reducing the CIB contamination of the thermal dust maps, we provide more accurate estimates of the local dust temperature and dust spectral index over the sky with reduced dispersion, especially at high Galactic latitudes above $b = pm 20{deg}$. We find that the dust temperature is $T = (19.4 pm 1.3)$ K and the dust spectral index is $beta = 1.6 pm 0.1$ averaged over the whole sky, while $T = (19.4 pm 1.5)$ K and $beta = 1.6 pm 0.2$ on 21 % of the sky at high latitudes. Moreover, subtracting the new CIB-removed thermal dust maps from the CMB-removed Planck maps gives access to the CIB anisotropies over 60 % of the sky at Galactic latitudes $|b| > 20{deg}$. Because they are a significant improvement over previous Planck products, the GNILC maps are recommended for thermal dust science. The new CIB maps can be regarded as indirect tracers of the dark matter and they are recommended for exploring cross-correlations with lensing and large-scale structure optical surveys. The reconstructed GNILC thermal dust and CIB maps are delivered as Planck products.
Aims: In this paper we present a case study to investigate conditions necessary to detect a characteristic magnetic field substructure embedded in a large-scale field. A helical magnetic field with a surrounding hourglass shaped field is expected from theoretical predictions and self-consistent magnetohydrodynamical (MHD) simulations to be present in the specific case of protostellar outflows. Hence, such an outflow environment is the perfect for our study. Methodes: We present synthetic polarisation maps in the infrared and millimeter regime of protostellar outflows performed with the newly developed RT and polarisation code POLARIS. The code, as the first, includes a self-consistent description of various alignement mechanism like the imperfect Davis-Greenstein (IDG) and the radiative torque (RAT) alignment. We investigate for which effects the grain size distribution, and applied alignement mechanism have. Results: We find that the IDG mechanism cannot produce any measurable polarization degree (< 1 %) whereas RAT alignment produced polarization degrees of a few 1 %. Furthermore, we developed a method to identify the origin of the polarization. We show that the helical magnetic field in the outflow can only be observed close to the outflow axis and at its tip, whereas in the surrounding regions the hourglass field in the foreground dominates the polarization. Furthermore, the polarization degree in the outflow lobe is lower than in the surroundings in agreement with observations. We also find that the orientation of the polarization vector flips around a few 100 micron due to the transition from dichroic extinction to thermal re-emission. Hence, in order to avoid ambiguities when interpreting polarization data, we suggest to observed in the far-infrared and mm regime. Finally, we show that with ALMA it is possible to observe the polarization emerging from protostellar outflows.
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