1. Automated algorithms to build active galactic nucleus classifiers
- Author
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Serena Falocco, Josefin Larsson, Francisco J. Carrera, Ministerio de Ciencia, Innovación y Universidades (España), European Commission, Agencia Estatal de Investigación (España), Alfred P. Sloan Foundation, and Department of Energy (US)
- Subjects
statistical [Methods] ,Cosmology and Nongalactic Astrophysics (astro-ph.CO) ,Active galactic nucleus ,active [Galaxies] ,Astrophysics::High Energy Astrophysical Phenomena ,media_common.quotation_subject ,FOS: Physical sciences ,Astrophysics::Cosmology and Extragalactic Astrophysics ,Set (abstract data type) ,Physics - Space Physics ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,Methods: statistical ,Astrophysics::Galaxy Astrophysics ,media_common ,Physics ,Astronomy and Astrophysics ,Galaxies: active ,Astrophysics - Astrophysics of Galaxies ,Space Physics (physics.space-ph) ,Redshift ,Galaxy ,Tree (data structure) ,Space and Planetary Science ,Sky ,Astrophysics of Galaxies (astro-ph.GA) ,Astrophysics - Instrumentation and Methods for Astrophysics ,Precision and recall ,Algorithm ,Classifier (UML) ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
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., FJC acknowledges financial support from the Spanish Ministry MCIU under project RTI2018-096686-B-C21 (MCIU/AEI/FEDER/UE), cofunded by FEDER funds and from the Agencia Estatal de Investigación, Unidad de Excelencia María de Maeztu, ref. MDM-2017- 0765. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions.
- Published
- 2021
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