Star cluster properties in two legus galaxies computed with stochastic stellar population synthesis models
We investigate a novel Bayesian analysis method, based on the Stochastically Lighting Up Galaxies (slug) code, to derive the masses, ages, and extinctions of star clusters from integrated light photometry. Unlike many analysis methods, slug correctly accounts for incomplete initial mass function (IM...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2015 October 20
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| In: |
The astrophysical journal
Year: 2015, Volume: 812, Issue: 2 |
| ISSN: | 1538-4357 |
| DOI: | 10.1088/0004-637X/812/2/147 |
| Online Access: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1088/0004-637X/812/2/147 Verlag, lizenzpflichtig, Volltext: https://iopscience.iop.org/article/10.1088/0004-637X/812/2/147 |
| Author Notes: | Mark R. Krumholz, Angela Adamo, Michele Fumagalli, Aida Wofford, Daniela Calzetti, Janice C. Lee, Bradley C. Whitmore, Stacey N. Bright, Kathryn Grasha, Dimitrios A. Gouliermis, Hwihyun Kim, Preethi Nair, Jenna E. Ryon, Linda J. Smith, David Thilker, Leonardo Ubeda, and Erik Zackrisson |
| Summary: | We investigate a novel Bayesian analysis method, based on the Stochastically Lighting Up Galaxies (slug) code, to derive the masses, ages, and extinctions of star clusters from integrated light photometry. Unlike many analysis methods, slug correctly accounts for incomplete initial mass function (IMF) sampling, and returns full posterior probability distributions rather than simply probability maxima. We apply our technique to 621 visually confirmed clusters in two nearby galaxies, NGC 628 and NGC 7793, that are part of the Legacy Extragalactic UV Survey (LEGUS). LEGUS provides Hubble Space Telescope photometry in the NUV, U, B, V, and I bands. We analyze the sensitivity of the derived cluster properties to choices of prior probability distribution, evolutionary tracks, IMF, metallicity, treatment of nebular emission, and extinction curve. |
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| Item Description: | Gesehen am 29.05.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1538-4357 |
| DOI: | 10.1088/0004-637X/812/2/147 |