OJ287 is the best candidate active galactic nucleus (AGN) for hosting a supermassive binary black hole (SMBBH) at very close separation. We present 120 Very Long Baseline Array (VLBA) observations (at 15 GHz) covering the time between April 1995 and April 2017. We find that the OJ287 radio jet is precessing on a time-scale of ∼22 yr. In addition, our data are consistent with a jet-axis rotation on a yearly time-scale. We model the precession (24 ± 2 yr) and combined motion of jet precession and jet-axis rotation. The jet motion explains the variability of the total radio flux-density via viewing angle changes and Doppler beaming. Half of the jet-precession time-scale is of the order of the dominant optical periodicity time-scale. We suggest that the optical emission is synchrotron emission and related to the jet radiation. The jet dynamics and flux-density light curves can be understood in terms of geometrical effects. Disturbances of an accretion disc caused by a plunging BH do not seem necessary to explain the observed variability. Although the SMBBH model does not seem necessary to explain the observed variability, an SMBBH or Lense-Thirring precession (disc around single BH) seem to be required to explain the time-scale of the precessing motion. Besides jet rotation also nutation of the jet axis could explain the observed motion of the jet axis. We find a strikingly similar scaling for the time-scales for precession and nutation as indicated for SS433 with a factor of roughly 50 times longer in OJ287.

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 with its onboard 10-m reflector radio telescope from the Baikonur cosmodrome on July 18, 2011. Together with some of the largest ground-based radio telescopes and a set of stations for tracking, collecting, and reducing the data obtained, this space radio telescope forms a multi-antenna groundspace radio interferometer with extremely long baselines, making it possible for the first time to study various objects in the Universe with angular resolutions a million times better than is possible with the human eye. The project is targeted at systematic studies of compact radio-emitting sources and their dynamics. Objects to be studied include supermassive black holes, accretion disks, and relativistic jets in active galactic nuclei, stellar-mass black holes, neutron stars and hypothetical quark stars, regions of formation of stars and planetary systems in our and other galaxies, interplanetary and interstellar plasma, and the gravitational field of the Earth. The results of ground-based and inflight tests of the space radio telescope carried out in both autonomous and ground-space interferometric regimes are reported. The derived characteristics are in agreement with the main requirements of the project. The astrophysical science program has begun.

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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 (SV M), 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, SV M and RF show a higher efficiency compared to LR and kNN.