1. Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection.
- Author
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Huang, Jian, Luo, Bin, Brandt, W. N., Chen, Ying, Ni, Qingling, Xue, Yongquan, and Zhang, Zijian
- Subjects
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STARS , *ASTRONOMICAL surveys , *MACHINE learning , *REDSHIFT , *GALAXIES , *QUASARS - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2025
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