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- Rodríguez, M. Jimena, et al.
(författare)
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PHANGS–JWST First Results : Dust-embedded Star Clusters in NGC 7496 Selected via 3.3 μm PAH Emission
- 2023
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Ingår i: Astrophysical Journal Letters. - : American Astronomical Society. - 2041-8205 .- 2041-8213. ; 944:2
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Tidskriftsartikel (refereegranskat)abstract
- The earliest stages of star formation occur enshrouded in dust and are not observable in the optical. Here we leverage the extraordinary new high-resolution infrared imaging from JWST to begin the study of dust-embedded star clusters in nearby galaxies throughout the Local Volume. We present a technique for identifying dust-embedded clusters in NGC 7496 (18.7 Mpc), the first galaxy to be observed by the PHANGS–JWST Cycle 1 Treasury Survey. We select sources that have strong 3.3 μm PAH emission based on a F300M − F335M color excess and identify 67 candidate embedded clusters. Only eight of these are found in the PHANGS-HST optically selected cluster catalog, and all are young (six have SED fit ages of ∼1 Myr). We find that this sample of embedded cluster candidates may significantly increase the census of young clusters in NGC 7496 from the PHANGS-HST catalog; the number of clusters younger than ∼2 Myr could be increased by a factor of 2. Candidates are preferentially located in dust lanes and are coincident with the peaks in the PHANGS-ALMA CO (2–1) maps. We take a first look at concentration indices, luminosity functions, SEDs spanning from 2700 Å to 21 μm, and stellar masses (estimated to be between ∼104 and 105 M⊙). The methods tested here provide a basis for future work to derive accurate constraints on the physical properties of embedded clusters, characterize the completeness of cluster samples, and expand analysis to all 19 galaxies in the PHANGS–JWST sample, which will enable basic unsolved problems in star formation and cluster evolution to be addressed.
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| 2. |
- Hannon, Stephen, et al.
(författare)
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Star cluster classification using deep transfer learning with PHANGS-HST
- 2023
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Ingår i: Monthly notices of the Royal Astronomical Society. - 0035-8711 .- 1365-2966. ; 526:2, s. 2991-3006
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Tidskriftsartikel (refereegranskat)abstract
- Currently available star cluster catalogues from the Hubble Space Telescope (HST) imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models that accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D ≲ 20 Mpc). Here, we use HST ultraviolet (UV)–optical imaging of over 20 000 sources in 23 galaxies from the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: (i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12, 14–18, and 18–24 Mpc), and (ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (∼60–80 per cent) and shows improvement by a factor of 2 in classifying asymmetric and multipeaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and spectral energy distribution (SED)-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ∼ 200 000) for public release.
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