Exploring the physical properties of Type II Quasar candidates at intermediate redshifts with CIGALE

Abstract

Active Galactic Nuclei (AGN) significantly influence galaxy evolution. Specific sources such as obscured AGNs, especially Type II quasars (QSO2), still remain understudied. We characterise 366 QSO2 candidates in the redshift desert (median z~1.1) identified via machine learning from SDSS/WISE photometry, analysing their spectral energy distributions (SEDs) and deriving their physical properties. Using CIGALE, we estimated star formation rate (SFR), stellar mass (M), AGN luminosity, and AGN fraction. We compared these with SPRITZ simulations and the literature, placing results in the galaxy evolution context. Our QSO2 candidates show diverse evolutionary stages. The SFR-M diagram reveals high-SFR sources above the main sequence, linking AGN activity to enhanced star formation. Quenched galaxies may indicate obscured star formation or AGN feedback. Additionally, the physical properties align with SPRITZ composite systems and AGN2, endorsing our obscured AGN classification. This study validates machine learning for identifying AGN-host galaxies, beyond traditional colour-colour selections. Diverse candidate properties highlight this method's ability to identify complex AGN systems. This advances our understanding of AGN-driven galaxy evolution with new target selection.