OJ287 is a quasi-periodic quasar with roughly 12 year optical cycles. It displays prominent outbursts that are predictable in a binary black hole model. The model predicted a major optical outburst in 2015 December. We found that the outburst did occur within the expected time range, peaking on 2015 December 5 at magnitude 12.9 in the optical R-band. Based on Swift/XRT satellite measurements and optical polarization data, we find that it included a major thermal component. Its timing provides an accurate estimate for the spin of the primary black hole, 0.313 0.01 c =  . The present outburst also confirms the established general relativistic properties of the system such as the loss of orbital energy to gravitational radiation at the 2% accuracy level, and it opens up the possibility of testing the black hole no-hair theorem with 10% accuracy during the present decade.

Results from regular monitoring of relativistic compact binaries like PSR 1913+16 are consistent with the dominant (quadrupole) order emission of gravitational waves (GWs). We show that observations associated with the binary black hole (BBH) central engine of blazar OJ287 demand the inclusion of gravitational radiation reaction effects beyond the quadrupolar order. It turns out that even the effects of certain hereditary contributions to GW emission are required to predict impact flare timings of OJ287. We develop an approach that incorporates this effect into the BBH model for OJ287. This allows us to demonstrate an excellent agreement between the observed impact flare timings and those predicted from ten orbital cycles of the BBH central engine model. The deduced rate of orbital period decay is nine orders of magnitude higher than the observed rate in PSR 1913+16, demonstrating again the relativistic nature of OJ287ʼs central engine. Finally, we argue that precise timing of the predicted 2019 impact flare should allow a test of the celebrated black hole "no-hair theorem" at the 10% level.

OpenBU http://open.bu.edu Astronomy BU Open Access Articles 2018-08-20 Stochastic modeling of multiwavelength variability of the classical BL Lac Object OJ287...

UVO 0825+15 is a hot bright helium-rich subdwarf which lies in K2 Field 5 and in a sample of intermediate helium-rich subdwarfs observed with Subaru/HDS. The K2 light curve shows low-amplitude variations, whilst the Subaru spectrum shows Pbiv absorption lines, indicative of a very high lead overabundance. UVO 0825+15 also has a high proper motion with kinematics typical for a thick disk star. Analyses of ultraviolet and intermediate dispersion optical spectra rule out a short-period binary companion, and provide fundamental atmospheric parameters of T eff = 38 900±270 K, log g/cm s −2 = 5.97 ± 0.11, log n He /n H = −0.57 ± 0.01, E B−V ≈ 0.03, and angular radius θ = 1.062±0.006×10−11 radians (formal errors). The high-resolution spectrum shows that carbon is > 2 dex subsolar, iron is approximately solar and all other elements heavier than argon are at least 2 -4 dex overabundant, including germanium, yttrium and lead. Approximately 150 lines in the blue-optical spectrum remain unidentified. The chemical structure of the photosphere is presumed to be determined by radiatively-dominated diffusion. The K2 light curve shows a dominant period around 10.8 h, with a variable amplitude, its first harmonic, and another period at 13.3 h. The preferred explanation is multi-periodic non-radial oscillation due to g-modes with very high radial order, although this presents difficulties for pulsation theory. Alternative explanations fail for lack of radial-velocity evidence. UVO 0825+15 represents the fourth member of a group of hot subdwarfs having helium-enriched photospheres and 3-4 dex overabundances of trans-iron elements, and is the first lead-rich subdwarf to show evidence of pulsations.

Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere-magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statistics and for supporting scientists to study auroral images in an objective, organized, and repeatable manner. Although previous studies have presented tools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with a high precision (>90%). This work considers seven auroral subclasses: breakup, colored, arcs, discrete, patchy, edge, and faint. Six different deep neural network architectures have been tested along with the well-known classification algorithms: k-nearest neighbor (KNN) and a support vector machine (SVM). A set of clean nighttime color auroral images, without clearly ambiguous auroral forms, moonlight, twilight, clouds, and so forth, were used for training and testing the classifiers. The deep neural networks generally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highest performance with an average classification precision of 92%.