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, and Zijian Zhang

TL;DR
This study employs machine learning to efficiently select type 1 quasars from photometric data, estimate their redshifts, and analyze the disk-corona connection, achieving high reliability and expanding understanding of quasar properties.
Contribution
The paper introduces a novel application of XGBoost for quasar selection and redshift estimation in the XMM-LSS field, and investigates the optical-X-ray luminosity relation.
Findings
High classification reliability (~99.9%) and good completeness (~87.5%).
Photometric redshifts range from 0.41 to 3.75 with ~17% outliers.
Confirmed the alpha_OX-L_2500 relation consistent with previous studies.
Abstract
We present photometric selection of type 1 quasars in the 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 () and good completeness (). 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 and the normalized median absolute deviation () is . To study the quasar…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Astrophysical Phenomena and Observations · Pulsars and Gravitational Waves Research
