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Título: | Automated algorithms to build active galactic nucleus classifiers |
Autor: | Falocco, Serena CSIC ORCID; Carrera, Francisco J. CSIC ORCID CVN ; Larsson, J. | Palabras clave: | Methods: statistical Galaxies: active |
Fecha de publicación: | 2022 | Editor: | Oxford University Press Royal Astronomical Society |
Citación: | Monthly Notices of the Royal Astronomical Society 510(1): 161-176 (2022) | Resumen: | We present a machine learning model to classify active galactic nuclei (AGNs) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test tree-based algorithms, using training samples built from the X-ray Multi-Mirror Mission–Newton (XMM–Newton) catalogue and the Sloan Digital Sky Survey (SDSS), with labels derived from the SDSS survey. The performance was tested making use of simulations and of cross-validation techniques. With a set of features including spectroscopic redshifts and X-ray parameters connected to source properties (e.g. fluxes and extension), as well as features related to X-ray instrumental conditions, the precision and recall for AGN identification are 94 and 93 per cent, while the type 1/2 classifier has a precision of 74 per cent and a recall of 80 per cent for type 2 AGNs. The performance obtained with photometric redshifts is very similar to that achieved with spectroscopic redshifts in both test cases, while there is a decrease in performance when excluding redshifts. Our machine learning model trained on X-ray features can accurately identify AGN in extragalactic surveys. The type 1/2 classifier has a valuable performance for type 2 AGNs, but its ability to generalize without redshifts is hampered by the limited census of absorbed AGN at high redshift. | Versión del editor: | https://doi.org/10.1093/mnras/stab3435 | URI: | http://hdl.handle.net/10261/279923 | DOI: | 10.1093/mnras/stab3435 | E-ISSN: | 1365-2966 |
Aparece en las colecciones: | (IFCA) Artículos |
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automatealgo.pdf | 2,23 MB | Adobe PDF | Visualizar/Abrir |
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