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

Wei, Wei and Huerta, E. A. and Whitmore, Bradley C and Lee, Janice C. and Hannon, Stephen and Chandar, Rupali and Dale, Daniel A. and Larson, Kirsten L. and Thilker, David A. and Ubeda, Leonardo and Boquien, Médéric and Chevance, Mélanie and Kruijssen, J. M. Diederik and Schruba, Andreas and Blanc, Guillermo A. and Congiu, Enrico (2020) Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey. Monthly Notices of the Royal Astronomical Society, 493 (3). pp. 3178-3193. ISSN 0035-8711. doi:10.1093/mnras/staa325.

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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≲20Mpc⁠) 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.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Wei, Wei0000-0002-1018-7708
Huerta, E. A.0000-0002-9682-3604
Lee, Janice C.0000-0002-2278-9407
Hannon, Stephen0000-0001-9628-8958
Dale, Daniel A.0000-0002-5782-9093
Larson, Kirsten L.0000-0003-3917-6460
Thilker, David A.0000-0002-8528-7340
Boquien, Médéric0000-0003-0946-6176
Chevance, Mélanie0000-0002-5635-5180
Kruijssen, J. M. Diederik0000-0002-8804-0212
Blanc, Guillermo A.0000-0003-4218-3944
Congiu, Enrico0000-0002-8549-4083
Additional Information:© 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( Accepted 2020 February 1. Received 2020 February 1; in original form 2019 August 29. Based on observations made with the NASA/ESA HST, obtained from the data archive at the Space Telescope Science Institute. STScI is operated by the Association of Universities for Research in Astronomy, Inc. under the National Aeronautics and Space Administration (NASA) contract NAS 5-26555. Support for Program number 15654 was provided through a grant from the STScI under NASA contract NAS5- 26555. This research has used the NASA/IPAC Extragalactic Database, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. EAH and WW gratefully acknowledge National Science Foundation (NSF) awards OAC-1931561 and OAC-1934757. This research is part of the Blue Waters sustained-petascale computing project, which is supported by NSF awards OCI-0725070 and ACI-1238993, and the State of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. This work utilized resources supported by the NSF’s Major Research Instrumentation program, grant OAC-1725729, as well as the University of Illinois at Urbana-Champaign. We are grateful to NVIDIA for donating several Tesla P100 and V100 GPUs that we used for our analysis, and the NSF grants NSF-1550514, NSF-1659702, and TG-PHY160053. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We thank the NCSA Gravity Group for useful feedback. MC and JMDK gratefully acknowledge funding from the Deutsche Forschungsgemeinschaft (DFG) through an Emmy Noether Research Group (grant KR4801/1-1) and the DFG Sachbeihilfe (grant KR4801/2-1). JMDK gratefully acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme via the ERC Starting grant MUSTANG (grant 714907).
Group:Infrared Processing and Analysis Center (IPAC)
Funding AgencyGrant Number
State of IllinoisUNSPECIFIED
Department of Energy (DOE)DE-AC02-06CH11357
Deutsche Forschungsgemeinschaft (DFG)KR4801/1-1
Deutsche Forschungsgemeinschaft (DFG)KR4801/2-1
European Research Council (ERC)714907
Subject Keywords:galaxies: star clusters: general
Issue or Number:3
Record Number:CaltechAUTHORS:20200430-151241621
Persistent URL:
Official Citation:Wei Wei, E A Huerta, Bradley C Whitmore, Janice C Lee, Stephen Hannon, Rupali Chandar, Daniel A Dale, Kirsten L Larson, David A Thilker, Leonardo Ubeda, Médéric Boquien, Mélanie Chevance, J M Diederik Kruijssen, Andreas Schruba, Guillermo A Blanc, Enrico Congiu, Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey, Monthly Notices of the Royal Astronomical Society, Volume 493, Issue 3, April 2020, Pages 3178–3193,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102951
Deposited By: George Porter
Deposited On:01 May 2020 18:08
Last Modified:16 Nov 2021 18:17

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