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Register a new userA Model For Polarised Microwave Foreground Emission From Interstellar Dust
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The upcoming generation of cosmic microwave background (CMB) experiments face a major challenge in detecting the weak cosmic B-mode signature predicted as a product of primordial gravitational waves. To achieve the required sensitivity these experiments must have impressive control of systematic effects and detailed understanding of the foreground emission that will influence the signal. In this paper, we present templates of the intensity and polarisation of emission from one of the main Galactic foregrounds, interstellar dust. These are produced using a model which includes a 3D description of the Galactic magnetic field, examining both large and small scales. We also include in the model the details of the dust density, grain alignment and the intrinsic polarisation of the emission from an individual grain. We present here Stokes parameter template maps at 150GHz and provide an on-line repository (http://www.imperial.ac.uk/people/c.contaldi/fgpol) for these and additional maps at frequencies that will be targeted by upcoming experiments such as EBEX, Spider and SPTpol.
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Templates for polarised emission from Galactic foregrounds at frequencies relevant to Cosmic Microwave Background (CMB) polarisation experiments are obtained by modelling the Galactic Magnetic Field (GMF) on large scales. This work extends the results of ODea et al. by including polarised synchrotron radiation as a source of foreground emission. The polarisation direction and fraction in this calculation are based solely on the underlying choice of GMF model and therefore provide an independent prediction for the polarisation signal on large scales. Templates of polarised foregrounds may be of use when forecasting effective experimental sensitivity. In turn, as measurements of the CMB polarisation over large fractions of the sky become routine, this model will allow for the data to constrain parameters in the, as yet, not well understood form of the GMF.
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 Cosmic Microwave Background anisotropies are difficult to measure at large angular scales. In this paper, we present a new analysis of the Planck High Frequency Instrument data that brings the cosmological part and its major foreground signal close to the detector noise. The solar dipole signal, induced by the motion of the solar system with respect to the CMB, is a very efficient tool to calibrate a detector or a set of detectors with high accuracy. In this work, the solar dipole signal is used to extract corrections of the frequency maps offsets reducing significantly uncertainties. The solar dipole parameters are refined together with the improvement of the high frequency foregrounds, and of the CMB large scales cosmological anisotropies. The stability of the solar dipole parameters is a powerful way to control the galactic foregrounds removal in the component separation process. It is used to build a model for Spectral Energy Distribution spatial variations of the interstellar dust emission. The knowledge of these variations will help future CMB analyses in intensity, and also in polarization to measure faint signal related to the optical reionization depth and the tensor-to-scalar ratio of the primordial anisotropies. The results of this work are: improved solar dipole parameters, a new interstellar dust model, and a large scale cosmological anisotropies map.
We report on the first direct measurement of the basic features of microwave radio emission from extensive air showers. Using a trigger provided by the KASCADE-Grande air shower array, the signals of the microwave antennas of the CROME (Cosmic-Ray Observation via Microwave Emission) experiment have been read out and searched for signatures of radio emission by high-energy air showers. Microwave signals have been detected for more than 30 showers with energies above $3times10^{16}$,eV. The observations presented in this Letter are consistent with a mainly forward-beamed, coherent and polarised emission process in the GHz frequency range. An isotropic, unpolarised radiation is disfavoured as the dominant emission model. The measurements show that microwave radiation offers a new means of studying air showers at very high energy.
Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning algorithms, a description of the statistical properties of such emission can be helpful. Here we examine a machine learning approach to inferring the statistical properties of dust from either observational data or physics-based simulations. In particular, we apply a type of neural network called a Variational Auto Encoder (VAE) to maps of the intensity of emission from interstellar dust as inferred from Planck sky maps and demonstrate its ability to a) simulate new samples with similar summary statistics as the training set, b) provide fits to emission maps withheld from the training set, and c) produce constrained realizations. We find VAEs are easier to train than another popular architecture: that of Generative Adversarial Networks (GANs), and are better-suited for use in Bayesian inference.
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