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Article: Automated classification of 2000 bright iras sources

TitleAutomated classification of 2000 bright iras sources
Authors
Gupta, RSingh, HPVolk, KKwok, S
KeywordsInfrared: Galaxies
Methods: Data Analysis
Issue Date2004
Citation
Astrophysical Journal, Supplement Series, 2004, v. 152 n. 2, p. 201-209 How to Cite?
DOI: http://dx.doi.org/10.1086/420967
AbstractAn artificial neural network (ANN) scheme has been employed that uses a supervised back-propagation algorithm to classify 2000 bright sources from the Calgary database of Infrared Astronomical Satellite (IRAS) spectra in the region 8-23 μm. The database has been classified into 17 predefined classes based on the spectral morphology. We have been able to classify over 80% of the sources correctly in the first instance. The speed and robustness of the scheme will allow us to classify the whole of the Low Resolution Spectrometer database, containing more than 50,000 sources, in the near future.
Persistent Identifierhttp://hdl.handle.net/10722/179693
ISSN
0067-0049
2021 Impact Factor: 9.200
2020 SCImago Journal Rankings: 3.546
ISI Accession Number ID
WOS:000221605200003
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorGupta, Ren_US
dc.contributor.authorSingh, HPen_US
dc.contributor.authorVolk, Ken_US
dc.contributor.authorKwok, Sen_US
dc.date.accessioned2012-12-19T10:02:52Z-
dc.date.available2012-12-19T10:02:52Z-
dc.date.issued2004en_US
dc.identifier.citationAstrophysical Journal, Supplement Series, 2004, v. 152 n. 2, p. 201-209en_US
dc.identifier.issn0067-0049en_US
dc.identifier.urihttp://hdl.handle.net/10722/179693-
dc.description.abstractAn artificial neural network (ANN) scheme has been employed that uses a supervised back-propagation algorithm to classify 2000 bright sources from the Calgary database of Infrared Astronomical Satellite (IRAS) spectra in the region 8-23 μm. The database has been classified into 17 predefined classes based on the spectral morphology. We have been able to classify over 80% of the sources correctly in the first instance. The speed and robustness of the scheme will allow us to classify the whole of the Low Resolution Spectrometer database, containing more than 50,000 sources, in the near future.en_US
dc.languageengen_US
dc.relation.ispartofAstrophysical Journal, Supplement Seriesen_US
dc.subjectInfrared: Galaxiesen_US
dc.subjectMethods: Data Analysisen_US
dc.titleAutomated classification of 2000 bright iras sourcesen_US
dc.typeArticleen_US
dc.identifier.emailKwok, S: deannote@hku.hken_US
dc.identifier.authorityKwok, S=rp00716en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1086/420967en_US
dc.identifier.scopuseid_2-s2.0-3142770461en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-3142770461&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume152en_US
dc.identifier.issue2en_US
dc.identifier.spage201en_US
dc.identifier.epage209en_US
dc.identifier.isiWOS:000221605200003-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridGupta, R=7501320703en_US
dc.identifier.scopusauthoridSingh, HP=35499554200en_US
dc.identifier.scopusauthoridVolk, K=7006571965en_US
dc.identifier.scopusauthoridKwok, S=22980498300en_US
dc.identifier.issnl0067-0049-

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