SRGz: Machine Learning Methods and Properties of the Catalog of SRG/eROSITA 
Point X-ray Source Optical Counterparts in the DESI Legacy Imaging Surveys 
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Meshcheryakov, A. V.; Borisov, V. D.; Khorunzhev, G. A.; Medvedev, P. A.; Gilfanov, M. R.; I.Belvedersky, M.; Sazonov, S. Yu.; Burenin, R. A.; Krivonos, R. A.; Bikmaev, I. F.; Khamitov, I. M.; Gerasimov, S. V.; Mashechkin, I. V.; Sunyaev, R. A.

doi:10.1134/S1063773723070022

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SRGz: Machine Learning Methods and Properties of the Catalog of SRG/eROSITA Point X-ray Source Optical Counterparts in the DESI Legacy Imaging Surveys Footprint

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Gilfanov, M. R.
High Energy Astrophysics, MPI for Astrophysics, Max Planck Society;

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Sunyaev, R. A.
High Energy Astrophysics, MPI for Astrophysics, Max Planck Society;

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Meshcheryakov, A. V., Borisov, V. D., Khorunzhev, G. A., Medvedev, P. A., Gilfanov, M. R., I.Belvedersky, M., et al. (2023). SRGz: Machine Learning Methods and Properties of the Catalog of SRG/eROSITA Point X-ray Source Optical Counterparts in the DESI Legacy Imaging Surveys Footprint. Astronomy Letters-A Journal of Astronomy and Space Astrophysics, 49(7), 359-409. doi:10.1134/S1063773723070022.

Cite as: https://hdl.handle.net/21.11116/0000-000E-0EBE-E

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