Photometric classification of emission line galaxies with machine-learning methods

Monthly Notices of the Royal Astronomical Society

Volumn 437, Issue 1, 2013, Pages 968-975

  Cavuoti, Stefano ^a,b   Brescia, Massimo ^a   D'Abrusco, Raffaele ^c   Longo, Giuseppe ^b,d   Paolillo, Maurizio ^b  

^a INAF OSSERVATORIO ASTROFISICO DI TORINO   (Italy)

^b UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^c HARVARD SMITHSONIAN CENTER FOR ASTROPHYSICS   (United States)

^d CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Catalogues; Galaxies: Active; Galaxies: seyfert; Methods: Data analysis; Surveys

Indexed keywords

ADAPTIVE OPTICS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA MINING; GALAXIES; LEARNING ALGORITHMS; LEARNING SYSTEMS; PARAMETER ESTIMATION; PHOTOMETRY;

ACTIVE GALACTIC NUCLEI; CATALOG; GALAXIES ACTIVE; GALAXY:SEYFERT; LEARNING-BASED METHODS; MACHINE LEARNING METHODS; METHODS. DATA ANALYSIS; OPTICAL-; PHOTOMETRICS; SEYFERTS;

SURVEYS;

EID: 84890014763     PISSN: 00358711     EISSN: 13652966     Source Type: Journal    
DOI: 10.1093/mnras/stt1961     Document Type: Article

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