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Star cluster classification in the PHANGS-HST survey: comparison between human and machine learning approaches

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 per...

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Main Authors: Whitmore, Bradley C. (Author) , Lee, Janice C. (Author) , Chandar, Rupali (Author) , Thilker, David A. (Author) , Hannon, Stephen (Author) , Wei, Wei (Author) , Huerta, E. A. (Author) , Bigiel, Frank (Author) , Boquien, Mederic (Author) , Chevance, Mélanie (Author) , Dale, Daniel A. (Author) , Deger, Sinan (Author) , Grasha, Kathryn (Author) , Klessen, Ralf S. (Author) , Kruijssen, Diederik (Author) , Larson, Kirsten L. (Author) , Mok, Angus (Author) , Rosolowsky, Erik (Author) , Schinnerer, Eva (Author) , Schruba, Andreas (Author) , Ubeda, Leonardo (Author) , Van Dyk, Schuyler D. (Author) , Watkins, Elizabeth J. (Author) , Williams, Thomas G. (Author)
Format: Article (Journal)
Language:English
Published: 21 July 2021
In: Monthly notices of the Royal Astronomical Society
Year: 2021, Volume: 506, Issue: 4, Pages: 5294-5317
ISSN:1365-2966
DOI:10.1093/mnras/stab2087
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/mnras/stab2087
Verlag, lizenzpflichtig, Volltext: https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=DynamicDOIArticle&SrcApp=WOS&KeyAID=10.1093%2Fmnras%2Fstab2087&DestApp=DOI&SrcAppSID=D5fwDyQuHoAzG9M7siP&SrcJTitle=MONTHLY+NOTICES+OF+THE+ROYAL+ASTRONOMICAL+SOCIETY&DestDOIRegistrantName=Oxford+University+Press
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Author Notes: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
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Summary: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.
Item Description:Gesehen am 26.01.2022
Physical Description:Online Resource
ISSN:1365-2966
DOI:10.1093/mnras/stab2087

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