Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk-Corona Connection

We present photometric selection of type 1 quasars in the ≈5.3 deg² XMM-Large Scale Structure survey field with machine learning. We constructed our training and blind-test samples using spectroscopically identified Sloan Digital Sky Survey quasars, galaxies, and stars. We utilized the XGBoost machine learning method to select a total of 1591 quasars. We assessed the classification performance based on the blind-test sample, and the outcome was favorable, demonstrating high reliability (≈99.9%) and good completeness (≈87.5%). We used XGBoost to estimate photometric redshifts of our selected quasars. The estimated photometric redshifts span a range from 0.41 to 3.75. The outlier fraction of these photometric redshift estimates is ≈17%, and the normalized median absolute deviation (σ_NMAD) is ≈0.07. To study the quasar disk-corona connection, we constructed a subsample of 1016 quasars with Hyper Suprime-Cam i < 22.5 after excluding radio-loud and potentially X-ray-absorbed quasars. The relation between the optical-to-X-ray power-law slope parameter (α_OX) and the 2500 Å monochromatic luminosity (L_2500Å) for this subsample is α OX = ( − 0.156 ± 0.007 ) log L 2500 Å + ( 3.175 ± 0.211 ) with a dispersion of 0.159. We found this correlation in good agreement with the correlations in previous studies. We explored several factors, which may bias the α_OX-L_2500Å relation, and found that their effects are not significant. We discussed possible evolution of the α_OX-L_2500Å relation with respect to L_2500Å or redshift.