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PHANGS-HST: new methods for star cluster identification in nearby galaxies

Thilker, David A. and Whitmore, Bradley C. and Lee, Janice C. and Deger, Sinan and Chandar, Rupali and Larson, Kirsten L. and Hannon, Stephen and Ubeda, Leonardo and Dale, Daniel A. and Glover, Simon C. O. and Grasha, Kathryn and Klessen, Ralf S. and Kruijssen, J. M. Diederik and Rosolowsky, Erik and Schruba, Andreas and White, Richard L. and Williams, Thomas G. (2022) PHANGS-HST: new methods for star cluster identification in nearby galaxies. Monthly Notices of the Royal Astronomical Society, 509 (3). pp. 4094-4127. ISSN 0035-8711. doi:10.1093/mnras/stab3183. https://resolver.caltech.edu/CaltechAUTHORS:20220309-966414000

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Abstract

We present an innovative and widely applicable approach for the detection and classification of stellar clusters, developed for the PHANGS-HST Treasury Program, an NUV-to-I band imaging campaign of 38 spiral galaxies. Our pipeline first generates a unified master source list for stars and candidate clusters, to enable a self-consistent inventory of all star formation products. To distinguish cluster candidates from stars, we introduce the Multiple Concentration Index (MCI) parameter, and measure inner and outer MCIs to probe morphology in more detail than with a single, standard concentration index (CI). We improve upon cluster candidate selection, jointly basing our criteria on expectations for MCI derived from synthetic cluster populations and existing cluster catalogues, yielding model and semi-empirical selection regions (respectively). Selection purity (confirmed clusters versus candidates, assessed via human-based classification) is high (up to 70 per cent) for moderately luminous sources in the semi-empirical selection region, and somewhat lower overall (outside the region or fainter). The number of candidates rises steeply with decreasing luminosity, but pipeline-integrated Machine Learning (ML) classification prevents this from being problematic. We quantify the performance of our PHANGS-HST methods in comparison to LEGUS for a sample of four galaxies in common to both surveys, finding overall agreement with 50–75 per cent of human verified star clusters appearing in both catalogues, but also subtle differences attributable to specific choices adopted by each project. The PHANGS-HST ML-classified Class 1 or 2 catalogues reach ∼1 mag fainter, ∼2 × lower stellar mass, and are 2−5 × larger in number, than attained in the human classified samples.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/stab3183DOIArticle
https://arxiv.org/abs/2106.13366arXivDiscussion Paper
https://archive.stsci.edu/hst/search_retrieve.htmlRelated ItemMikulski Archive for Space Telescopes
https://archive.stsci.edu/hlsp/phangs-hstRelated ItemPHANGS-HST
https://doi.org/10.17909/t9-r08f-dq31DOIPHANGS-HST
ORCID:
AuthorORCID
Thilker, David A.0000-0002-8528-7340
Whitmore, Bradley C.0000-0002-3784-7032
Lee, Janice C.0000-0002-2278-9407
Deger, Sinan0000-0003-1943-723X
Chandar, Rupali0000-0003-0085-4623
Larson, Kirsten L.0000-0003-3917-6460
Hannon, Stephen0000-0001-9628-8958
Dale, Daniel A.0000-0002-5782-9093
Glover, Simon C. O.0000-0001-6708-1317
Grasha, Kathryn0000-0002-3247-5321
Klessen, Ralf S.0000-0002-0560-3172
Kruijssen, J. M. Diederik0000-0002-8804-0212
Rosolowsky, Erik0000-0002-5204-2259
Williams, Thomas G.0000-0002-0012-2142
Additional Information:© 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Accepted 2021 October 28. Received 2021 October 27; in original form 2021 June 24. Based on observations made with the NASA/ESA Hubble Space Telescope, 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 NASA contract NAS 5-26555. Support for Program number 15654 was provided through a grant from the STScI under NASA contract NAS5-26555. Most of the plots in this paper were generated with TOPCAT (Taylor 2005) and/or its sister command-line package STILTS (Taylor 2006), both developed and generously released/maintained for public use by Mark Taylor. A significant amount of interactive data exploration and testing was conducted using TOPCAT. Our pipeline makes extensive use of the following software packages: DOLPHOT, photutils, astropy, matplotlib, numpy, IMFIT, pytorch, and CIGALE. We extend our appreciation to their respective developers. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. JMDK gratefully acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through an Emmy Noether Research Group (grant number KR4801/1-1) and the DFG Sachbeihilfe (grant number KR4801/2-1), as well as from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme via the ERC Starting Grant MUSTANG (grant agreement number 714907). SCOG and RSK acknowledge support from the DFG via SFB 881 ‘The Milky Way System’ (sub-projects A1, B1, B2, and B8) and from the Heidelberg cluster of excellence EXC 2181-390900948 ‘STRUCTURES: A unifying approach to emergent phenomena in the physical world, mathematics, and complex data’, funded by the German Excellence Strategy. They also acknowledge funding from the European Research Council via the ERC Synergy Grant ‘ECOGAL’ (grant 855130). TGW acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 694343). DATA AVAILABILITY. The imaging observations underlying this article can be retrieved from the Mikulski Archive for Space Telescopes at https://archive.stsci.edu/hst/search_retrieve.html under proposal GO-15654. High level science products, including science ready mosaicked imaging, associated with HST GO-15654 are provided at https://archive.stsci.edu/hlsp/phangs-hst with digital object identifier doi:10.17909/t9-r08f-dq31.
Group:Infrared Processing and Analysis Center (IPAC), TAPIR
Funders:
Funding AgencyGrant Number
NASANAS 5-26555
NASA/JPL/CaltechUNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)KR4801/1-1
Deutsche Forschungsgemeinschaft (DFG)KR4801/2-1
European Research Council (ERC)714907
Deutsche Forschungsgemeinschaft (DFG)SFB 881
Deutsche Forschungsgemeinschaft (DFG)EXC 2181-390900948
European Research Council (ERC)855130
European Research Council (ERC)694343
Subject Keywords:methods: data analysis, catalogues, surveys, galaxies: individual NGC 628, NGC 1433, NGC 1566, NGC 3351, galaxies: star clusters: general
Issue or Number:3
DOI:10.1093/mnras/stab3183
Record Number:CaltechAUTHORS:20220309-966414000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220309-966414000
Official Citation:David A Thilker, Bradley C Whitmore, Janice C Lee, Sinan Deger, Rupali Chandar, Kirsten L Larson, Stephen Hannon, Leonardo Ubeda, Daniel A Dale, Simon C O Glover, Kathryn Grasha, Ralf S Klessen, J M Diederik Kruijssen, Erik Rosolowsky, Andreas Schruba, Richard L White, Thomas G Williams, PHANGS-HST: new methods for star cluster identification in nearby galaxies, Monthly Notices of the Royal Astronomical Society, Volume 509, Issue 3, January 2022, Pages 4094–4127, https://doi.org/10.1093/mnras/stab3183
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113847
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:11 Mar 2022 00:14
Last Modified:11 Mar 2022 00:14

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