Machine learning applications for AGN studies

We show that the redshift, z, bolometric luminosity, LBol, central mass of the supermassive black hole (SMBH), MBH, Eddington ratio, {lambda}Edd, and AGN class (obscured or unobscured) can be reconstructed through multi-wavelength photometric observations only. We trained a random forest regressor (RFR) ML-model on 7616 spectroscopically observed AGNs from the SPIDERS- AGN survey, which had previously been cross-matched with soft X-ray observations (from ROSAT or XMM), WISE mid-infrared photometry, and optical photometry from SDSS ugriz filters.

We built a catalog of 21 050 AGNs that were subsequently reconstructed with the trained RFR; for 9687 sources, we found archival redshift measurements. All AGNs were classified as either type 1 or type 2 using a random forest classifier (RFC) algorithm on a subset of known sources. All known photometric measurement uncertainties were incorporated via a simulation-based approach.