Bar morphologies of HSC-SSP disc galaxies

We utilise a Deep Learning model, fine-tuned on Galaxy Zoo volunteer classifications, to identify (strongly and weakly) barred and unbarred disc galaxies in Hyper Suprime-Cam Subaru Strategic Program i-band images. We select AGN using three independent diagnostics: mid-infrared colours, X-ray detections, and spectral energy distribution (SED) fitting.

The SED analysis, performed using CIGALE, quantifies the relative AGN contribution to the total galaxy luminosity (f_AGN_) and the AGN luminosity (L_disc_). We assess the impact of bars by comparing AGN incidence and properties in barred galaxies against carefully constructed redshift-, stellar mass-, and colour-matched unbarred control samples. Our binary AGN classification experiment demonstrates that barred disc galaxies host a higher fraction of AGN compared to their unbarred counterparts, though the significance depends on the AGN selection method, with a more modest excess for SED AGN, and control sample size.