Exploring the physical properties of type II quasar candidates at intermediate redshifts with CIGALE

Context. Active galactic nuclei (AGNs) play a vital role in the evolution of galaxies over cosmic time, significantly influencing their star formation and growth. As obscured AGNs are difficult to identify due to obscuration by gas and dust, our understanding of their full impact is still under study. It is essential to investigate their properties and distribution, in particular type II quasars (QSO2s), to comprehensively account for AGN populations and understand how their fraction evolves over time. Such studies provide critical insights into the co-evolution of AGNs and their host galaxies. Aims. Following our previous study, where a machine learning approach was applied to identify 366 QSO2 candidates from SDSS and WISE surveys (median z ∼ 1.1), we now aim to characterise this QSO2 candidate sample by analysing their spectral energy distributions (SEDs) and deriving their physical properties. Methods. We estimated relevant physical properties of the QSO2 candidates, including the star formation rate (SFR), stellar mass ( M * ), AGN luminosity, and AGN fraction, using SED fitting with CIGALE . We compared the inferred properties with analogous populations in the semi-empirical simulation SPRITZ , placing these results in the context of galaxy evolution. Results. The physical properties derived for our QSO2 candidates indicate a diverse population of AGNs at various stages of evolution. QSO2 candidates cover a wide range in the SFR– M * diagram, with numerous high-SFR sources lying above the main sequence at their redshift, suggesting a link between AGN activity and enhanced star formation. Additionally, we identify a population of apparently quenched galaxies, which may be due to obscured star formation or AGN feedback. Furthermore, the physical parameters of our sample align closely with those of composite systems and type 2 AGNs from SPRITZ , supporting the classification of these candidates as obscured AGNs. Conclusions. This study confirms that our QSO2 candidates, selected via a machine learning approach, exhibit properties consistent with being AGN-host galaxies. This method can identify AGNs within large galaxy samples by considering AGN fractions and their contributions to the infrared luminosity, going beyond the limitations of traditional colour–colour selection techniques. The diverse properties of our candidates demonstrate the capability of this approach to identify complex AGN-host systems that might otherwise be missed. This shows the help that machine learning can provide in refining AGN classifications and advancing our understanding of galaxy evolution driven by AGN activity with new target selection.