The Russian Academy of Sciences and Federal Space Agency, together with the participation of many international organizations, worked toward the launch of the RadioAstron orbiting space observatory...

We present full-track high-resolution radio observations of the jet of the galaxy M87 at 8 and 15 GHz. These observations were taken over three consecutive days in 2009 May using the Very Long Baseline Array (VLBA), one antenna of the Very Large Array (VLA), and the Effelsberg 100 m telescope. Our produced images have dynamic ranges exceeding 20 000:1 and resolve linear scales down to approximately 100 Schwarzschild radii, revealing a limb-brightened jet and a faint, steep spectrum counter-jet. We performed jet-to-counter-jet analysis, which helped estimate the physical parameters of the flow. The rich internal structure of the jet is dominated by three helical threads, likely produced by the Kelvin–Helmholtz (KH) instability developing in a supersonic flow with a Mach number of approximately 20 and an enthalpy ratio of around 0.3. We produce a clean imaging bias-corrected 8–15 GHz spectral index image, which shows spectrum flattening in regions of helical thread intersections. This further supports the KH origin of the observed internal structure of the jet. We detect polarized emission in the jet at distances of approximately 20 milliarcseconds from the core and find Faraday rotation which follows a transverse gradient across the jet. We apply Faraday rotation correction to the polarization position angle and find that the position angle changes as a function of distance from the jet axis, which suggests the presence of a helical magnetic field.

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM and RF show a higher efficiency compared to LR and kNN.

We analysed the parsec-scale linear polarization properties of 436 active galactic nuclei (AGN) based on 15 GHz polarimetric Very Long Baseline Array (VLBA) observations. We present polarization and total intensity images averaged over at least five epochs since 1996 January 19 through 2019 August 4. Stacking improves the image sensitivity down to ∼30 μJy beam⁻¹ and effectively fills out the jet cross-section both in total intensity and linear polarization. It delineates the long-term persistent magnetic field configuration and its regularity by restoring spatial distributions of the electric vector position angle (EVPA) and fractional polarization, respectively. On average, about ten years of stacking period is needed to reveal the stable and most-complete polarization distribution of a source. We find that the degree of polarization significantly increases down and across the jet towards its edges, typically manifesting U or W-shaped transverse profiles, suggesting a presence of a large-scale helical magnetic field associated with the outflow. In some AGN jets, mainly BL Lacs, we detect quasi-constant fractional polarization profiles across the jet, accompanied by EVPAs that closely follow the outflow. BL Lacs show higher fractional polarization values in their cores and jets than those in quasars up to hectoparsec de-projected scales, while on larger scales, they become comparable. High-synchrotron-peaked BL Lac jets are found to be less polarized than intermediate and low-synchrotron-peaked BL Lacs. The spatial distribution of the EVPAs in BL Lacs tend to align with the local jet direction, while quasars show an excess of orthogonal polarization orientation.