We present a study of the active galactic nucleus (AGN) activity in the local Universe (z < 0.33) and its correlation with the host galaxy properties, derived from an Sloan Digital Sky Survey (SDSS DR8) sample with spectroscopic star-formation rate (SFR) and stellar mass ($\mathcal {M}_{\ast }$) determination. To quantify the level of AGN activity we used X-ray information from the XMM-Newton Serendipitous Source Catalogue (3XMM DR8). Applying multiwavelength AGN selection criteria (optical BPT-diagrams, X-ray/optical ratio etc) we found that 24 per cent of the detected sources are efficiently-accreting AGN with moderate-to-high X-ray luminosity, which are twice as likely to be hosted by star-forming galaxies than by quiescent ones. The distribution of the specific Black Hole accretion rate (sBHAR, λsBHAR) shows that nuclear activity in local, non-AGN dominated galaxies peaks at very low accretion rates (−4 ≲ log λsBHAR ≲ −3) in all stellar mass ranges. However, we observe systematically larger values of sBHAR for galaxies with active star-formation than for quiescent ones, as well as an increase of the mean λsBHAR with SFR for both star-forming and quiescent galaxies. These findings confirm the decreased level of AGN activity with cosmic time and are consistent with a scenario where both star-formation and AGN activity are fuelled by a common gas reservoir.

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Context. Modern sky surveys are producing ever larger amounts of observational data, which makes the application of classical approaches for the classification and analysis of objects challenging and time-consuming. However, this issue may be significantly mitigated by the application of automatic machine and deep learning methods. Aims. We propose ULISSE, a new deep learning tool that, starting from a single prototype object, is capable of identifying objects sharing the same morphological and photometric properties, and hence of creating a list of candidate sosia. In this work, we focus on applying our method to the detection of AGN candidates in a Sloan Digital Sky Survey galaxy sample, since the identification and classification of Active Galactic Nuclei (AGN) in the optical band still remains a challenging task in extragalactic astronomy. Methods. Intended for the initial exploration of large sky surveys, ULISSE directly uses features extracted from the ImageNet dataset to perform a similarity search. The method is capable of rapidly identifying a list of candidates, starting from only a single image of a given prototype, without the need for any time-consuming neural network training. Results. Our experiments show ULISSE is able to identify AGN candidates based on a combination of host galaxy morphology, color and the presence of a central nuclear source, with a retrieval efficiency ranging from 21 % to 65 % (including composite sources) depending on the prototype, where the random guess baseline is 12 %. We find ULISSE to be most effective in retrieving AGN in early-type host galaxies, as opposed to prototypes with spiral-or late-type properties. Conclusions. Based on the results described in this work, ULISSE can be a promising tool for selecting different types of astrophysical objects in current and future wide-field surveys (e.g. Euclid, LSST etc.) that target millions of sources every single night.

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We present a new approach to the composite spectra construction based on stacking spectra with similar slopes α λ within the wavelength range redward of Lyα emission line, which allows to reduce the typical noise. With the help of this technique a detailed study of the HI Lyα-forest region (λ rest ≈ 1050 − 1200 Å) of the own sample of 3439 medium-resolution quasar spectra from SDSS DR7 was performed. More than 14 lines were found within this wavelength range, three of which were known from previous studies of quasar composite spectra from SDSS and some others were known in composite spectra from space-based telescopes or high-resolution spectra of individual quasars from ground-based telescopes.