Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey

• Published in: Monthly notices of the Royal Astronomical Society Vol. 493; no. 3; pp. 3178 - 3193
• Main Authors: Wei, Wei, Huerta, E A, Whitmore, Bradley C, Lee, Janice C, Hannon, Stephen, Chandar, Rupali, Dale, Daniel A, Larson, Kirsten L, Thilker, David A, Ubeda, Leonardo, Boquien, Médéric, Chevance, Mélanie, Kruijssen, J M Diederik, Schruba, Andreas, Blanc, Guillermo A, Congiu, Enrico
• Format: Journal Article
• Language: English
• Published: United Kingdom Oxford University Press 01.04.2020
• Subjects: galaxies: star clusters: general
• DOI: 10.1093/mnras/staa325
• ISSN: 0035-8711

ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.