Machine learning applications for the study of AGN physical properties using photometric observations

Abstract

We investigate the physical nature of Active Galactic Nuclei using machine learning tools. We show that the redshift $z$, the bolometric luminosity $L_{\rm Bol}$, the central mass of the supermassive black hole $M_{\rm BH}$, the Eddington ratio $\lambda_{\rm Edd}$ as well as the AGN class (obscured or unobscured) can be reconstructed through multi-wavelength photometric observations only. A Support Vector Regression (SVR) ML-model is trained on 7616 of spectroscopically observed AGN from the SPIDERS-AGN survey, previously cross-matched with soft X-ray observations (from ROSAT or XMM), WISE mid-infrared photometry, and optical photometry from SDSS $ugriz$ filters. We build a catalogue of 21364 AGN to be reconstructed with the trained SVR: for 9944 sources, we found archival redshift measurements. All AGN are classified as either Type 1/2 using a Random Forest (RF) algorithm on a subset of known sources. All known photometric measurement uncertainties are incorporated using a simulation-based approach. We present the reconstructed catalogue of 21364 AGN with redshifts ranging from $0 < z < 2.5$. $z$ estimations are made for 11420 new sources, with an outlier rate within 10%. Type 1/2 AGN can be identified with respective efficiencies of 88% and 93%: the estimated classification of all sources is given in the dataset. $L_{\rm Bol}$, $M_{\rm BH}$, and $\lambda_{\rm Edd}$ values are given for 16907 new sources with their estimated error. These results have been made publicly available. The release of this catalogue will advance AGN studies by presenting key parameters of the accretion history of 6 dex in luminosity over a wide range of $z$. Similar applications of ML techniques using photometric data only will be essential in the future, with large datasets from eROSITA, JSWT and the VRO poised to be released in the next decade.Comment: 20 pages, 24 figures, submitted to A&