Papers › Photometric Selection of type 1 Quasars in the XMM-LSS Field with Machine Learning and...
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
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We present photometric selection of type 1 quasars in the ≈5.3 deg² XMM-Large Scale Structure (XMM-LSS) survey field with machine learning. We constructed our training and \hbox{blind-test} samples using spectroscopically identified SDSS quasars, galaxies, and stars. We utilized the XGBoost machine learning method to select a total of 1\,591 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 1\,016 quasars with HSC 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 Angstrom monochromatic luminosity (L₂₅₀₀) for this subsample is α_(OX)=(-0.156±0.007) log L₂₅₀₀+(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)-L₂₅₀₀ relation and found that their effects are not significant. We discussed possible evolution of the α_(OX)-L₂₅₀₀ relation with respect to L₂₅₀₀ or redshift.
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