Spectroscopic Confirmation of Obscured AGN Populations from Unsupervised Machine Learning

Raphael E. Hviding, Kevin N. Hainline, Andy D. Goulding, Jenny E. Greene

Research output: Contribution to journalArticlepeer-review


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 photometry. AGN candidates are chosen without incorporating prior AGN selection criteria and are fainter, redder, and more numerous, ∼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 mostly comprised 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 growth of supermassive black holes. This approach is well suited to upcoming photometric surveys, such as Euclid, Rubin, and Roman.

Original languageEnglish (US)
Article number169
JournalAstronomical Journal
Issue number4
StatePublished - Apr 1 2024

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


Dive into the research topics of 'Spectroscopic Confirmation of Obscured AGN Populations from Unsupervised Machine Learning'. Together they form a unique fingerprint.

Cite this