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DC Field | Value | Language |
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dc.citation.endPage | 5390 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 5377 | - |
dc.citation.title | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | - |
dc.citation.volume | 492 | - |
dc.contributor.author | Luo, Shengda | - |
dc.contributor.author | Leung, Alex P. | - |
dc.contributor.author | Hui, C. Y. | - |
dc.contributor.author | Li, K. L. | - |
dc.date.accessioned | 2023-12-21T17:45:29Z | - |
dc.date.available | 2023-12-21T17:45:29Z | - |
dc.date.created | 2021-05-04 | - |
dc.date.issued | 2020-03 | - |
dc.description.abstract | We have investigated a number of factors that can have significant impacts on the classification performance of gamma-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of automatic feature selection can construct a simple model with a small set of features that yields better performance over previous results. Secondly, because of the small sample size of the training/test sets of certain classes in gamma-ray, nested re-sampling and cross-validations are suggested for quantifying the statistical fluctuations of the quoted accuracy. We have also constructed a test set by cross matching the identified active galactic nuclei (AGNs) and the pulsars (PSRs) in the FermiLAT 8-yr point source catalogue (4FGL) with those unidentified sources in the previous 3rd Fermi-LAT Source Catalog (3FGL). Using this cross-matched set, we show that some features used for building classification model with the identified source can suffer from the problem of covariate shift, which can be a result of various observational effects. This can possibly hamper the actual performance when one applies such model in classifying unidentified sources. Using our framework, both AGN/PSR and young pulsar (YNG)/millisecond pulsar (MSP) classifiers are automatically updated with the new features and the enlarged training samples in 4FGL catalogue incorporated. Using a two-layer model with these updated classifiers, we have selected 20 promising MSP candidates with confidence scores : 98 per cent from the unidentified sources in 4FGL catalogue that can provide inputs for a multiwavelength identification campaign. | - |
dc.identifier.bibliographicCitation | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, v.492, no.4, pp.5377 - 5390 | - |
dc.identifier.doi | 10.1093/mnras/staa166 | - |
dc.identifier.issn | 0035-8711 | - |
dc.identifier.scopusid | 2-s2.0-85095278350 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/52832 | - |
dc.identifier.wosid | 000518148000059 | - |
dc.language | 영어 | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.title | An investigation on the factors affecting machine learning classifications in gamma-ray astronomy | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Astronomy & Astrophysics | - |
dc.relation.journalResearchArea | Astronomy & Astrophysics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | methods: statistical | - |
dc.subject.keywordAuthor | pulsars: general | - |
dc.subject.keywordAuthor | gamma-rays: stars | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | RANDOM FORESTS | - |
dc.subject.keywordPlus | REGRESSION | - |
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