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

Abstract

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.

Authors:

Wei, Wei ^[1]; Huerta, E. A. ^[2]; Whitmore, Bradley C. ^[3]; Lee, Janice C. ^[4]; Hannon, Stephen  ^[5];

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• Search DOE PAGES for ORCID "0000-0001-9628-8958"

Chandar, Rupali ^[6]; Dale, Daniel A. ^[7]; Larson, Kirsten L. ^[4]; Thilker, David A.  ^[8];

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• Search DOE PAGES for ORCID "0000-0002-8528-7340"

Ubeda, Leonardo ^[3]; Boquien, Médéric ^[9]; Chevance, Mélanie ^[10]; Kruijssen, J. M. Diederik  ^[10];

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Schruba, Andreas ^[11]; Blanc, Guillermo A. ^[12]; Congiu, Enrico  ^[13]

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• Search DOE PAGES for ORCID "0000-0002-8549-4083"

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+ Show Author Affiliations

1. NCSA, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
2. NCSA, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
3. Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
4. Caltech/IPAC, California Institute of Technology, Pasadena, CA 91125, USA
5. Department of Physics and Astronomy, University of California, Riverside, CA 92507, USA
6. Department of Physics and Astronomy, University of Toledo, Toledo, OH 43606, USA
7. Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071, USA
8. Department of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA
9. Centro de Astronomía, Universidad de Antofagasta, Avenida Angamos 601, Antofagasta 1270300, Chile
10. Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Grabengasse 1, D-69117 Heidelberg, Germany
11. Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse 1, D-85748 Garching, Germany
12. Observatories of the Carnegie Institution for Science, Pasadena, CA 91101, USA, Departamento de Astronomía, Universidad de Chile, Casilla 36-D, Santiago, Chile
13. Observatories of the Carnegie Institution for Science, Pasadena, CA 91101, USA, Las Campanas Observatory, Colina El Pino, Casilla 601, La Serena, Chile

Publication Date:

Tue Feb 04 00:00:00 EST 2020

Sponsoring Org.:

USDOE

OSTI Identifier:

1604098

Grant/Contract Number:  

AC02-06CH11357

Resource Type:

Published Article

Journal Name:

Monthly Notices of the Royal Astronomical Society

Additional Journal Information:

Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 493 Journal Issue: 3; Journal ID: ISSN 0035-8711

Publisher:

Oxford University Press

Country of Publication:

United Kingdom

Language:

English