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Status: Bibliographieeintrag

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Verfasst von:Whitmore, Bradley C. [VerfasserIn]   i
 Lee, Janice C. [VerfasserIn]   i
 Chandar, Rupali [VerfasserIn]   i
 Thilker, David A. [VerfasserIn]   i
 Hannon, Stephen [VerfasserIn]   i
 Wei, Wei [VerfasserIn]   i
 Huerta, E. A. [VerfasserIn]   i
 Bigiel, Frank [VerfasserIn]   i
 Boquien, Mederic [VerfasserIn]   i
 Chevance, Mélanie [VerfasserIn]   i
 Dale, Daniel A. [VerfasserIn]   i
 Deger, Sinan [VerfasserIn]   i
 Grasha, Kathryn [VerfasserIn]   i
 Klessen, Ralf S. [VerfasserIn]   i
 Kruijssen, Diederik [VerfasserIn]   i
 Larson, Kirsten L. [VerfasserIn]   i
 Mok, Angus [VerfasserIn]   i
 Rosolowsky, Erik [VerfasserIn]   i
 Schinnerer, Eva [VerfasserIn]   i
 Schruba, Andreas [VerfasserIn]   i
 Ubeda, Leonardo [VerfasserIn]   i
 Van Dyk, Schuyler D. [VerfasserIn]   i
 Watkins, Elizabeth J. [VerfasserIn]   i
 Williams, Thomas G. [VerfasserIn]   i
Titel:Star cluster classification in the PHANGS-HST survey
Titelzusatz:comparison between human and machine learning approaches
Verf.angabe:Bradley C. Whitmore, Janice C. Lee, Rupali Chandar, David A. Thilker, Stephen Hannon, Wei Wei, E.A. Huerta, Frank Bigiel, Mederic Boquien, Melanie Chevance, Daniel A. Dale, Sinan Deger, Kathryn Grasha, Ralf S. Klessen, J.M. Diederik Kruijssen, Kirsten L. Larson, Angus Mok, Erik Rosolowsky, Eva Schinnerer, Andreas Schruba, Leonardo Ubeda, Schuyler D. Van Dyk, Elizabeth Watkins and Thomas Williams
E-Jahr:2021
Jahr:21 July 2021
Umfang:24 S.
Fussnoten:Gesehen am 26.01.2022
Titel Quelle:Enthalten in: Royal Astronomical SocietyMonthly notices of the Royal Astronomical Society
Ort Quelle:Oxford : Oxford Univ. Press, 1827
Jahr Quelle:2021
Band/Heft Quelle:506(2021), 4, Seite 5294-5317
ISSN Quelle:1365-2966
Abstract:When completed, the PHANGS-HST project will provide a census of roughly 50000 compact star clusters and associations, as well as human morphological classifications for roughly 20000 of those objects. These large numbers motivated the development of a more objective and repeatable method to help perform source classifications. In this paper, we consider the results for five PHANGS-HST galaxies (NGC 628, NGC 1433, NGC 1566, NGC 3351, NGC 3627) using classifications from two convolutional neural network architectures (RESNET and VGG) trained using deep transfer learning techniques. The results are compared to classifications performed by humans. The primary result is that the neural network classifications are comparable in quality to the human classifications with typical agreement around 70 to 80 per cent for Class 1 clusters (symmetric, centrally concentrated) and 40 to 70 per cent for Class 2 clusters (asymmetric, centrally concentrated). If Class 1 and 2 are considered together the agreement is 82 +/- 3 per cent. Dependencies on magnitudes, crowding, and background surface brightness are examined. A detailed description of the criteria and methodology used for the human classifications is included along with an examination of systematic differences between PHANGS-HST and LEGUS. The distribution of data points in a colour-colour diagram is used as a 'figure of merit' to further test the relative performances of the different methods. The effects on science results (e.g. determinations of mass and age functions) of using different cluster classification methods are examined and found to be minimal.
DOI:doi:10.1093/mnras/stab2087
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1093/mnras/stab2087
 Volltext: https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=DynamicDOIArticle&SrcApp=WOS&KeyAID=10.1093%2 ...
 DOI: https://doi.org/10.1093/mnras/stab2087
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:age
 camera
 catalogues
 evolution
 galaxies
 galaxies: star clusters: general
 globular-clusters
 legus
 mass
 ob associations
 space-telescope observations
 stellar clusters
K10plus-PPN:1787233235
Verknüpfungen:→ Zeitschrift

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