Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection - Jian Huang; Bin Luo; W. N. Brandt; Ying Chen; Qingling Ni; Yongquan Xue; Zijian Zhang (The Astrophysical Journal)
We present photometric selection of type 1 quasars in the ≈5.3 deg ^2 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 ${α }_{{\rm{OX}}}=(-0.156± 0.007)\,{\rm{log}}\,L_{2500\,\mathring{\rm A} }+(3.175± 0.211)$ 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.