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

P. A. C. Cunha, A. Humphrey, J. Brinchmann, A. Paulino-Afonso, L. Bisigello, M. Bolzonella, D. Vaz

First listed 2025-03-05 | Last updated 2025-03-05

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.

Short digest

CIGALE SED fits are used to derive SFR, M*, AGN luminosity, and AGN fraction for 366 machine‑learned Type II quasar candidates in the redshift desert (median z≈1.1), then benchmarked against SPRITZ populations and literature samples. On the SFR–M* plane many candidates sit above the main sequence at their redshift, while a tail appears quenched, pointing to episodes of enhanced star formation alongside possible feedback or obscured SF. The inferred properties align with SPRITZ composite systems and AGN2, supporting their obscured‑AGN nature and validating machine‑learning selection beyond simple colour–colour cuts.

Key figures to inspect

• Figure 1 (stacked SDSS spectrum): Check narrow high‑ionization [Ne V] and [O II] visibility and Gaussian fits to verify Type II line signatures and stacking S/N across rest‑frame coverage.
• Figure 2 (frac_AGN trends): Inspect how M* and specific L_AGN/M vary with frac_AGN, and where sources cross the Leja et al. (2018) threshold—useful for identifying a high‑frac_AGN tail and typical host masses.
• Figure 3 (SFR–M* plane): Quantify the fraction above the Schreiber et al. (2015) main sequence at z≈1.1 and compare offsets against zCOSMOS AGN2 contours and the SDSS control to see the elevated‑SFR locus.
• Figure 4 (ΔSFR from MS): Compare ΔSFR distributions for the QSO2s versus Bongiorno et al. (2012) and Zakamska et al. (2003) to visualize both the starbursting and quenched tails relative to the main sequence scatter.