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Magnetohydrodynamic (MHD) turbulence is a cross-field process relevant to many systems. A prerequisite for understanding these systems is to constrain the role of MHD turbulence, and in particular, the energy exchange between kinetic and magnetic forms. The energetics of strongly magnetized and compressible turbulence has so far resisted attempts to understand them. Numerical simulations reveal that kinetic energy can be orders of magnitude higher than fluctuating magnetic energy. We solved this lack-of-balance puzzle by calculating the energetics of compressible and sub-Alfvénic turbulence based on the dynamics of coherent cylindrical fluid parcels. Using the MHD Lagrangian, we proved analytically that the bulk of the magnetic energy transferred to kinetic energy is the energy that is stored in the coupling between the ordered and fluctuating magnetic field. The analytical relations are in strikingly good agreement with numerical data, up to second-order terms. 

Context. Optical polarimeters are typically calibrated using measurements of stars with known and stable polarization parameters. However, there is a lack of such stars available across the sky. Many of the currently available standards are not suitable for medium and large telescopes due to their high brightness. Moreover, as we find, some of the polarimetric standards used are in fact variable or have polarization parameters that differ from their cataloged values. Aims. Our goal is to establish a sample of stable standards suitable for calibrating linear optical polarimeters with an accuracy down to 10 −3 in fractional polarization. Methods. For 4 yr, we have been running a monitoring campaign of a sample of standard candidates comprised of 107 stars distributed across the northern sky. We analyzed the variability of the linear polarization of these stars, taking into account the non-Gaussian nature of fractional polarization measurements. For a subsample of nine stars, we also performed multiband polarization measurements. Results. We created a new catalog of 65 stars (see Table 2) that are stable, have small uncertainties of measured polarimetric parameters, and can be used as calibrators of polarimeters at medium and large telescopes. 

We present the first Bayesian method for tomographic decomposition of the plane-of-sky orientation of the magnetic field with the use of stellar polarimetry and distance. This standalone tomographic inversion method presents an important step forward in reconstructing the magnetized interstellar medium (ISM) in three dimensions within dusty regions. We develop a model in which the polarization signal from the magnetized and dusty ISM is described by thin layers at various distances, a working assumption which should be satisfied in small-angular circular apertures. Our modeling makes it possible to infer the mean polarization (amplitude and orientation) induced by individual dusty clouds and to account for the turbulence-induced scatter in a generic way. We present a likelihood function that explicitly accounts for uncertainties in polarization and parallax. We develop a framework for reconstructing the magnetized ISM through the maximization of the log-likelihood using a nested sampling method. We test our Bayesian inversion method on mock data, representative of the high Galactic latitude sky, taking into account realistic uncertainties from Gaia and as expected for the optical polarization survey P ASIPHAE according to the currently planned observing strategy. We demonstrate that our method is effective at recovering the cloud properties as soon as the polarization induced by a cloud to its background stars is higher than ~0.1% for the adopted survey exposure time and level of systematic uncertainty. The larger the induced polarization is, the better the method’s performance, and the lower the number of required stars. Our method makes it possible to recover not only the mean polarization properties but also to characterize the intrinsic scatter, thus creating new ways to characterize ISM turbulence and the magnetic field strength. Finally, we apply our method to an existing data set of starlight polarization with known line-of-sight decomposition, demonstrating agreement with previous results and an improved quantification of uncertainties in cloud properties.