We report 6 yr monitoring of a distant bright quasar CTS C30.10 (z = 0.90052) with the Southern African Large Telescope (SALT). We measured the rest-frame time-lag of 562±2 days between the continuum variations and the response of the Mg II emission line, using the Javelin approach. More conservative approach, based on five different methods, imply the time delay of 564 +109 −71 days. This time delay, combined with other available measurements of Mg II line delay, mostly for lower redshift sources, shows that the Mg II line reverberation implies a radius-luminosity relation very similar to the one based on a more frequently studied Hβ line.

Context. The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, have been recently cross-matched to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were separated from stars and quasars through colour cuts, which may leave imperfections because different source types may overlap in colour space. Aims. The aim of the present work is to identify galaxies in the WISE × SuperCOSMOS catalogue through an alternative approach of machine learning. This allows us to define more complex separations in the multi-colour space than is possible with simple colour cuts, and should provide a more reliable source classification. Methods. For the automatised classification we used the support vector machines (SVM) learning algorithm and employed SDSS spectroscopic sources that we cross-matched with WISE × SuperCOSMOS to construct the training and verification set. We performed a number of tests to examine the behaviour of the classifier (completeness, purity, and accuracy) as a function of source apparent magnitude and Galactic latitude. We then applied the classifier to the full-sky data and analysed the resulting catalogue of candidate galaxies. We also compared the resulting dataset with the one obtained through colour cuts. Results. The tests indicate very high accuracy, completeness, and purity (>95%) of the classifier at the bright end; this deteriorates for the faintest sources, but still retains acceptable levels of ∼85%. No significant variation in the classification quality with Galactic latitude is observed. When we applied the classifier to all-sky WISE × SuperCOSMOS data, we found 15 million galaxies after masking problematic areas. The resulting sample is purer than the one produced by applying colour cuts, at the price of a lower completeness across the sky. Conclusions. The automatic classification is a successful alternative approach to colour cuts for defining a reliable galaxy sample. The identifications we obtained are included in the public release of the WISE × SuperCOSMOS galaxy catalogue.

Context. The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. Classifying them reliably is, however, a challenging task owing to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Simple colour cuts are often not sufficient; for satisfactory levels of completeness and purity, more sophisticated classification methods are needed. Aims. Here we aim to obtain comprehensive and reliable star, galaxy, and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those which are reliably measured for the majority of sources. Methods. For the automatic classification we applied a supervised machine learning algorithm, support vector machines (SVM). It requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset (DR10) for that purpose. We tested the performance of two kernels used by the classifier, and determined the minimum number of sources in the training set required to achieve stable classification, as well as the minimum dimension of the parameter space. We also tested SVM classification accuracy as a function of extinction and apparent magnitude. Thus, the calibrated classifier was finally applied to all-sky WISE data, flux-limited to 16 mag (Vega) in the 3.4 µm channel. Results. By calibrating on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnitude, W1 − W2 colour, and a differential aperture magnitude) we obtained very good classification efficiency in all the tests. At the bright end, the completeness for stars and galaxies reaches ∼95%, deteriorating to ∼80% at W1 = 16 mag, while for quasars it stays at a level of ∼95% independently of magnitude. Similar numbers are obtained for purity. Application of the classifier to full-sky WISE data and appropriate a posteriori cleaning allowed us to obtain catalogues of star and galaxy candidates that appear reliable. However, the sources flagged by the classifier as "quasars" are in fact dominated by dusty galaxies; they also exhibit contamination from sources located mainly at low ecliptic latitudes, consistent with solar system objects.