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 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 mac...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/ad9baf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592618083581952 |
|---|---|
| author | Jian Huang Bin Luo W. N. Brandt Ying Chen Qingling Ni Yongquan Xue Zijian Zhang |
| author_facet | Jian Huang Bin Luo W. N. Brandt Ying Chen Qingling Ni Yongquan Xue Zijian Zhang |
| author_sort | Jian Huang |
| collection | DOAJ |
| description | 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 ${\alpha }_{{\rm{OX}}}=(-0.156\pm 0.007)\,{\rm{log}}\,{L}_{2500\,\mathring{\rm A} }+(3.175\pm 0.211)$ 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. |
| format | Article |
| id | doaj-art-07c12a1820fe4d809fd5191e3c8796ba |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-07c12a1820fe4d809fd5191e3c8796ba2025-01-21T06:03:49ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01979210710.3847/1538-4357/ad9bafPhotometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona ConnectionJian Huang0https://orcid.org/0000-0002-9335-9455Bin Luo1https://orcid.org/0000-0002-9036-0063W. N. Brandt2https://orcid.org/0000-0002-0167-2453Ying Chen3https://orcid.org/0009-0007-3422-8758Qingling Ni4Yongquan Xue5https://orcid.org/0000-0002-1935-8104Zijian Zhang6https://orcid.org/0000-0002-2420-5022School of Astronomy and Space Science, Nanjing University , Nanjing, Jiangsu 210093, People’s Republic of China; Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University) , Ministry of Education, Nanjing 210093, People’s Republic of ChinaSchool of Astronomy and Space Science, Nanjing University , Nanjing, Jiangsu 210093, People’s Republic of China; Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University) , Ministry of Education, Nanjing 210093, People’s Republic of ChinaDepartment of Astronomy & Astrophysics, 525 Davey Lab, The Pennsylvania State University , University Park, PA 16802, USA; Institute for Gravitation and the Cosmos, The Pennsylvania State University , University Park, PA 16802, USA; Department of Physics, 104 Davey Lab, The Pennsylvania State University , University Park, PA 16802, USASchool of Astronomy and Space Science, Nanjing University , Nanjing, Jiangsu 210093, People’s Republic of China; Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University) , Ministry of Education, Nanjing 210093, People’s Republic of ChinaMax-Planck-Institut für extraterrestrische Physik (MPE) , Gießenbachstraße 1, D-85748 Garching bei München, GermanyCAS Key Laboratory for Research in Galaxies and Cosmology, Department of Astronomy, University of Science and Technology of China , Hefei 230026, People’s Republic of China; School of Astronomy and Space Sciences, University of Science and Technology of China , Hefei 230026, People’s Republic of ChinaSchool of Astronomy and Space Science, Nanjing University , Nanjing, Jiangsu 210093, People’s Republic of China; Department of Astronomy, School of Physics, Peking University , Beijing 100871, People’s Republic of ChinaWe 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 ${\alpha }_{{\rm{OX}}}=(-0.156\pm 0.007)\,{\rm{log}}\,{L}_{2500\,\mathring{\rm A} }+(3.175\pm 0.211)$ 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.https://doi.org/10.3847/1538-4357/ad9bafQuasarsX-ray surveys |
| spellingShingle | Jian Huang Bin Luo W. N. Brandt Ying Chen Qingling Ni Yongquan Xue Zijian Zhang Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection The Astrophysical Journal Quasars X-ray surveys |
| title | Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection |
| title_full | Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection |
| title_fullStr | Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection |
| title_full_unstemmed | Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection |
| title_short | Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection |
| title_sort | photometric selection of type 1 quasars in the xmm lss field with machine learning and the disk corona connection |
| topic | Quasars X-ray surveys |
| url | https://doi.org/10.3847/1538-4357/ad9baf |
| work_keys_str_mv | AT jianhuang photometricselectionoftype1quasarsinthexmmlssfieldwithmachinelearningandthediskcoronaconnection AT binluo photometricselectionoftype1quasarsinthexmmlssfieldwithmachinelearningandthediskcoronaconnection AT wnbrandt photometricselectionoftype1quasarsinthexmmlssfieldwithmachinelearningandthediskcoronaconnection AT yingchen photometricselectionoftype1quasarsinthexmmlssfieldwithmachinelearningandthediskcoronaconnection AT qinglingni photometricselectionoftype1quasarsinthexmmlssfieldwithmachinelearningandthediskcoronaconnection AT yongquanxue photometricselectionoftype1quasarsinthexmmlssfieldwithmachinelearningandthediskcoronaconnection AT zijianzhang photometricselectionoftype1quasarsinthexmmlssfieldwithmachinelearningandthediskcoronaconnection |