Effective Radii of Young, Massive Star Clusters in Two LEGUS Galaxies<sup>∗</sup>

Ryon, J. E.; Gallagher, John S.; Smith, L. J.; Adamo, Angela; Calzetti, Daniela; Bright, Stacey N.; Cignoni, M.; Cook, D.; Dale, Daniel A.; Elmegreen, Bruce; Fumagalli, Michele; Gouliermis, D. A.; Grasha, Kathryn; Grebel, Eva K.; Kim, Hwi; Messa, Matteo; Thilker, David A.; Úbeda, Leonardo

doi:10.3847/1538-4357/aa719e

Cited by 91 publications

(125 citation statements)

References 69 publications

(143 reference statements)

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“…9 we show the mass and stellar half-mass radius of each of the simulated clusters at a simulation time of t = 175 Myr, separating the young clusters (darker red symbols) from the older cluster population (lighter red symbols). The simulated clusters are compared to the half-light radii and bestfit masses of clusters in the LMC, SMC, Fornax dwarf spheroidal and the Milky Way, obtained from McLaugh- (2005) and for clusters in two LEGUS galaxies from Ryon et al (2017). The minimum cluster mass used in Subfind and the spatial resolution are indicated with horizontal solid and vertical lines.…”

Section: Size and Surface Densitymentioning

confidence: 99%

The GRIFFIN Project—Formation of Star Clusters with Individual Massive Stars in a Simulated Dwarf Galaxy Starburst

Lahén

Naab

Johansson

et al. 2020

ApJ

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We describe a population of young star clusters (SCs) formed in a hydrodynamical simulation of a gasrich dwarf galaxy merger resolved with individual massive stars at sub-parsec spatial resolution. The simulation is part of the griffin (Galaxy Realizations Including Feedback From INdividual massive stars) project. The star formation environment during the simulation spans seven orders of magnitude in gas surface density and thermal pressure, and the global star formation rate surface density (Σ SFR ) varies by more than three orders of magnitude during the simulation. Young SCs more massive than M * ,cl ∼ 10 2.5 M form along a mass function with a power-law index α ∼ −1.7 (α ∼ −2 for M * ,cl 10 3 M ) at all merger phases, while the normalization and the highest SC masses (up to ∼ 10 6 M ) correlate with Σ SFR . The cluster formation efficiency varies from Γ ∼ 20% in early merger phases to Γ ∼ 80% at the peak of the starburst and is compared to observations and model predictions. The massive SCs ( 10 4 M ) have sizes and mean surface densities similar to observed young massive SCs. Simulated lower mass clusters appear slightly more concentrated than observed. All SCs form on timescales of a few Myr and lose their gas rapidly resulting in typical stellar age spreads between σ ∼ 0.1 − 2 Myr (1σ), consistent with observations. The age spreads increase with cluster mass, with the most massive cluster (∼ 10 6 M ) reaching a spread of 5 Myr once its hierarchical formation finishes. Our study shows that it is now feasible to investigate the SC population of entire galaxies with novel high-resolution numerical simulations.

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Section: Size and Surface Densitymentioning

confidence: 99%

The GRIFFIN Project—Formation of Star Clusters with Individual Massive Stars in a Simulated Dwarf Galaxy Starburst

Lahén

Naab

Johansson

et al. 2020

ApJ

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“…The objects of interest in this study are compact star clusters and stellar associations in galaxies at distances between 4 Mpc to 20 Mpc. The physical sizes of these objects are characterized by effective radii between 0.5pc to about 5pc (Portegies Zwart et al 2010;Ryon et al 2017). Hence, only with the resolution of HST (1.2 pc at D=4 Mpc) can clusters be distinguished from individual stars and separated from other star clusters in galaxies beyond the Local Group.…”

Section: Classification Of Compact Star Clusters In Nearby Galaxiesmentioning

confidence: 99%

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

Wei

Huerta

Whitmore

et al. 2020

Monthly Notices of the Royal Astronomical Society

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133

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Deep learning is rapidly becoming a ubiquitous signal-processing tool in big-data experiments. Here, we present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies (D 20 Mpc) in the PHANGS-HST survey. Given the relatively small and unbalanced nature of existing, human-labelled star cluster datasets, 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 show that human classification is at the 66% : 37% : 40% : 61% agreement level for the four classes considered. On the other hand, our findings indicate that deep learning algorithms achieve 76% : 63% : 59% : 70% for a star cluster sample within 4Mpc ≤ D ≤ 10Mpc. We further tested the robustness of our deep learning algorithms to generalize to different cluster images. For this experiment we used the first data obtained by PHANGS-HST of NGC1559, which is more distant at D = 19Mpc, and found that deep learning produces classification accuracies 73% : 42% : 52% : 67%. We furnish evidence for the robustness of these analyses by using two different state-of-the-art neural network models for image classification, which were trained multiple times from the ground up to assess the variance and stability of our results. Through ablation studies, we quantified the importance of the NUV, U, B, V and I images for morphological classification with our deep learning models, and find that, as expected, the V-band is the key contributor as human classifications are based on images taken in that filter. The methods introduced in this article lay the foundations to automate classification for these objects at scale, and motivate the creation of a standardized star cluster classification dataset, developed and agreed upon by a range of experts in the field.

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“…Their typical size ranges between 1 − 10 pc (e.g. Lada & Lada 2003;Ryon et al 2015Ryon et al , 2017 even if the most massive ones ( 10 6 M ), usually called super star clusters, can extend up to ∼ 20 pc (e.g. Meurer et al 1995; Bastian et al 2013).…”

Section: Sizesmentioning

confidence: 99%

Star-forming clumps in the Lyman Alpha Reference Sample of galaxies – I. Photometric analysis and clumpiness

Messa

Adamo

Östlin

et al. 2019

Monthly Notices of the Royal Astronomical Society

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We study young star-forming clumps on physical scales of 10 − 500 pc in the Lyman-Alpha Reference Sample (LARS), a collection of low-redshift (z = 0.03 − 0.2) UVselected star-forming galaxies. In each of the 14 galaxies of the sample, we detect clumps for which we derive sizes and magnitudes in 5 UV-optical filters. The final sample includes ∼ 1400 clumps, of which ∼ 600 have magnitude uncertainties below 0.3 in all filters. The UV luminosity function for the total sample of clumps is described by a power-law with slope α = −2.03 +0.11 −0.13 . Clumps in the LARS galaxies have on average Σ SFR values higher than what observed in HII regions of local galaxies and comparable to typical SFR densities of clumps in z = 1 − 3 galaxies. We derive the clumpiness as the relative contribution from clumps to the UV emission of each galaxy, and study it as a function of galactic-scale properties, i.e. Σ SFR and the ratio between rotational and dispersion velocities of the gas (v s /σ 0 ). We find that in galaxies with higher Σ SFR or lower v s /σ 0 , clumps dominate the UV emission of their host systems. All LARS galaxies with Lyα escape fractions larger than 10% have more than 50% of the UV luminosity from clumps. We tested the robustness of these results against the effect of different physical resolutions. At low resolution, the measured clumpiness appears more elevated than if we could resolve clumps down to single clusters. This effect is small in the redshift range covered by LARS, thus our results are not driven by the physical resolution.

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Effective Radii of Young, Massive Star Clusters in Two LEGUS Galaxies^∗

Cited by 91 publications

References 69 publications

The GRIFFIN Project—Formation of Star Clusters with Individual Massive Stars in a Simulated Dwarf Galaxy Starburst

The GRIFFIN Project—Formation of Star Clusters with Individual Massive Stars in a Simulated Dwarf Galaxy Starburst

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

Star-forming clumps in the Lyman Alpha Reference Sample of galaxies – I. Photometric analysis and clumpiness

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Effective Radii of Young, Massive Star Clusters in Two LEGUS Galaxies∗

Cited by 91 publications

References 69 publications

The GRIFFIN Project—Formation of Star Clusters with Individual Massive Stars in a Simulated Dwarf Galaxy Starburst

The GRIFFIN Project—Formation of Star Clusters with Individual Massive Stars in a Simulated Dwarf Galaxy Starburst

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

Star-forming clumps in the Lyman Alpha Reference Sample of galaxies – I. Photometric analysis and clumpiness

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Effective Radii of Young, Massive Star Clusters in Two LEGUS Galaxies^∗