Galaxy Zoo: Reproducing galaxy morphologies via machine learning

Monthly Notices of the Royal Astronomical Society

Volumn 406, Issue 1, 2010, Pages 342-353

  Banerji, Manda ^a,b   Lahav, Ofer ^a   Lintott, Chris J ^c   Abdalla, Filipe B ^a   Schawinski, Kevin ^d,e   Bamford, Steven P ^f   Andreescu, Dan ^g   Murray, Phil ^h   Raddick, M Jordan ⁱ   Slosar, Anze ^j   Szalay, Alex ⁱ   Thomas, Daniel ^k   Vandenberg, Jan ⁱ  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c Denys Wilkinson Laboratory   (United Kingdom)

^d Yale University   (United States)

^e YALE UNIVERSITY   (United States)

^f UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^g LinkLab   (United States)

^h Fingerprint Digital Media   (Ireland)

ⁱ JOHNS HOPKINS UNIVERSITY   (United States)

^j LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^k UNIVERSITY OF PORTSMOUTH   (United Kingdom)

more..

Author keywords

Galaxies: general; Methods: data analysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; SURVEYS;

CLASSIFIEDS; EARLY TYPES; GALAXIES GENERAL; INPUT PARAMETER; MACHINE LEARNING ALGORITHMS; METHODS. DATA ANALYSIS; MORPHOLOGICAL CLASSIFICATIONS; NEURAL-NETWORKS; POINT-SOURCES; TRAINING SETS;

GALAXIES;

EID: 77955128781     PISSN: 00358711     EISSN: 13652966     Source Type: Journal    
DOI: 10.1111/j.1365-2966.2010.16713.x     Document Type: Article

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