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. We find that slug's results for individual clusters are insensitive to most of these choices, but that the posterior probability distributions we derive are often quite broad, and sometimes multi-peaked and quite sensitive to the choice of priors. In contrast, the properties of the cluster population as a whole are relatively robust against all of these choices. We also compare our results from slug to those derived with a conventional non-stochastic fitting code, Yggdrasil. We show that slug's stochastic models are generally a better fit to the observations than the deterministic ones used by Yggdrasil. However, the overall properties of the cluster populations recovered by both codes are qualitatively similar.

Krumholz, M., Adamo, A., Fumagalli, M., Wofford, A., Calzetti, D., Lee, J., et al. (2015). STAR CLUSTER PROPERTIES in TWO LEGUS GALAXIES COMPUTED with STOCHASTIC STELLAR POPULATION SYNTHESIS MODELS. THE ASTROPHYSICAL JOURNAL, 812(2), 147 [10.1088/0004-637X/812/2/147].

STAR CLUSTER PROPERTIES in TWO LEGUS GALAXIES COMPUTED with STOCHASTIC STELLAR POPULATION SYNTHESIS MODELS

Michele Fumagalli;
2015

Abstract

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. We find that slug's results for individual clusters are insensitive to most of these choices, but that the posterior probability distributions we derive are often quite broad, and sometimes multi-peaked and quite sensitive to the choice of priors. In contrast, the properties of the cluster population as a whole are relatively robust against all of these choices. We also compare our results from slug to those derived with a conventional non-stochastic fitting code, Yggdrasil. We show that slug's stochastic models are generally a better fit to the observations than the deterministic ones used by Yggdrasil. However, the overall properties of the cluster populations recovered by both codes are qualitatively similar.
Articolo in rivista - Articolo scientifico
galaxies: individual (NGC 628, NGC 7793); galaxies: star clusters: general; methods: data analysis; methods: statistical; techniques: photometric
English
2015
812
2
147
147
open
Krumholz, M., Adamo, A., Fumagalli, M., Wofford, A., Calzetti, D., Lee, J., et al. (2015). STAR CLUSTER PROPERTIES in TWO LEGUS GALAXIES COMPUTED with STOCHASTIC STELLAR POPULATION SYNTHESIS MODELS. THE ASTROPHYSICAL JOURNAL, 812(2), 147 [10.1088/0004-637X/812/2/147].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/280521
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