Spectroscopic Confirmation of Obscured AGN Populations from Unsupervised Machine Learning

Abstract

We present the result of a spectroscopic campaign targeting Active Galactic Nucleus (AGN) candidates selected using a novel unsupervised machine-learning (ML) algorithm trained on optical and mid-infrared (mid-IR) photometry. AGN candidates are chosen without incorporating prior AGN selection criteria and are fainter, redder, and more numerous, $\sim$340 AGN deg$^{-2}$, than comparable photometric and spectroscopic samples. In this work we obtain 178 rest-optical spectra from two candidate ML-identified AGN classes with the Hectospec spectrograph on the MMT Observatory. We find that our first ML-identified group, is dominated by Type I AGNs (85%) with a $<3$% contamination rate from non-AGNs. Our second ML-identified group is comprised mostly of Type II AGNs (65%) with a moderate contamination rate of 15% primarily from star-forming galaxies. Our spectroscopic analyses suggest that the classes recover more obscured AGNs, confirming that ML techniques are effective at recovering large populations of AGNs at high levels of extinction. We demonstrate the efficacy of pairing existing WISE data with large-area and deep optical/near-infrared photometric surveys to select large populations of AGNs and recover obscured SMBH growth. This approach is well suited to upcoming photometric surveys, such as Euclid, Rubin, and Roman.