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Register a new userEvidence for Line-of-Sight Frequency Decorrelation of Polarized Dust Emission in $Planck$ Data
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If a single line of sight (LOS) intercepts multiple dust clouds of different spectral energy distributions and magnetic field orientations, the frequency scaling of each of the Stokes $Q$ and $U$ parameters of thermal dust emission may be different (LOS frequency decorrelation). We present first evidence for LOS frequency decorrelation in $Planck$ data. We use independent, neutral-hydrogen--measurements of the number of clouds per LOS and the magnetic field orientation in each cloud to select two sets of sightlines: (i) a target sample (pixels likely to exhibit LOS frequency decorrelation); (ii) a control sample (pixels lacking complex LOS structure). We test the null hypothesis that LOS frequency decorrelation is not detectable in $Planck$ 353 and 217~GHz polarization data at high Galactic latitudes. The data reject this hypothesis at high significance. The detection is robust against choice of CMB map and map-making pipeline. The observed change in polarization angle due to LOS frequency decorrelation is detectable above the $Planck$ noise level. The probability that the detected effect is due to noise alone ranges from $5times 10^{-2}$ to $4times 10^{-7}$, depending on the CMB subtraction algorithm and treatment of residual systematics; correcting for residual systematics increases the significance of the effect. The LOS decorrelation effect is stronger for sightlines with more misaligned magnetic fields, as expected. We estimate that an intrinsic variation of $sim15%$ in the ratio of 353 to 217~GHz polarized emission between clouds is sufficient to reproduce the measured effect. Our finding underlines the importance of ongoing studies to map the 3D structure of the magnetized dusty ISM that could help component separation methods to account for frequency decorrelation effects in CMB polarization studies.
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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.
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.
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 dust properties inferred from the analysis of Planck observations in total and polarized emission challenge current dust models. We propose new dust models compatible with polarized and unpolarized data in extinction and emission for translucent lines of sight ($0.5 < A_V < 2.5$). We amended the DustEM tool to model polarized extinction and emission. We fit the spectral dependence of the mean extinction, polarized extinction, SED and polarized SED with PAHs, astrosilicates and amorphous carbon (a-C). The astrosilicate population is aligned along the magnetic field lines, while the a-C population may be aligned or not. With their current optical properties, oblate astrosilicate grains are not emissive enough to reproduce the emission to extinction polarization ratio $P_{353}/p_V$ derived with Planck data. Models using prolate astrosilicate grains with an elongation $a/b=3$ and an inclusion of 20% of porosity succeed. The spectral dependence of the polarized SED is steeper in our models than in the data. Models perform slightly better when a-C grains are aligned. A small (6%) volume inclusion of a-C in the astrosilicate matrix removes the need for porosity and perfect grain alignment, and improves the fit to the polarized SED. Dust models based on astrosilicates can be reconciled with Planck data by adapting the shape of grains and adding inclusions of porosity or a-C in the astrosilicate matrix.
Recently, the Planck satellite found a larger and most precise value of the matter energy density, that impacts on the present values of other cosmological parameters such as the Hubble constant, the present cluster abundances and the age of the Universe. The existing tension between Planck determination of these parameters in the frame of the base LambdaCDM model and their direct measurements generated lively discussions and several interpretations. In this paper we quantify this tension by exploring several extensions of the base LambdaCDM model that include the leptonic asymmetry. We set bounds on the radiation content of the Universe and neutrino properties by using the latest cosmological measurements, imposing also self-consistent BBN constraints on the primordial helium abundance. For all cosmological asymmetric models we find the preference of cosmological data for smaller values of active and sterile neutrino masses. This increases the tension between cosmological and short baseline neutrino oscillation data that favor a sterile neutrino with the mass of around 1 eV. For the case of degenerate massive neutrinos, we find that the discrepancies with direct determinations of the Hubble constant, the present cluster abundances and the age of the Universe are alleviated at ~ 1.3 sigma for all leptonic asymmetric models. We also find ~2 sigma statistical evidence of the preference of cosmological data for the normal neutrino hierarchy. This is more evident for the case of cosmological models involving leptonic asymmetry and three massive neutrino species. We conclude that the current cosmological data favor the leptonic asymmetric extension of the base LambdaCDM model and normal neutrino mass hierarchy over the models with additional sterile neutrino species and/or inverted neutrino mass hierarchy.
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