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Star Clusters: Constraining Gas Clearing Timescales with HST Hα Imaging and Classifying Cluster Morphology with Machine Learning

Presentation #130.06D in the session Star Associations.

Published onJun 29, 2022
Star Clusters: Constraining Gas Clearing Timescales with HST Hα Imaging and Classifying Cluster Morphology with Machine Learning

This dissertation talk presents two studies focusing on the properties of star clusters as resolved in HST imaging taken by the LEGUS and PHANGS surveys:

First, the analysis of star cluster ages in tandem with the detailed morphology of any associated HII regions can provide insight into the processes that clear a cluster’s natal gas, as well as the accuracy of cluster ages and dust extinction derived from Spectral Energy Distribution (SED) fitting. We classify 3757 star clusters in 16 nearby galaxies according to their Hα morphology (concentrated, partially exposed, no emission), using HST imaging from LEGUS. We find: 1) The mean SED ages of clusters with concentrated (1-2 Myr) and partially exposed HII region morphologies (2-3 Myr) indicate a relatively early onset of gas clearing and a short (1-2 Myr) clearing timescale. 2) The extinction of clusters can be overestimated due to the presence of red supergiants, which is a result of stochastic sampling of the IMF in low mass clusters. 3) The age-reddening degeneracy impacts the results of the SED fitting — out of 169 clusters with M* ≥ 5000 solar masses, 46 have SED ages which appear significantly underestimated or overestimated based on their environment, and the presence or absence of Hα. 4) Lastly, for galaxies at 3-10 Mpc, we find that uncertainties in morphological classification due to distance-dependent resolution effects do not affect overall conclusions on gas clearing timescales when using HST Hα images, whereas ground-based images do not provide sufficient resolution for the analysis.

Secondly, the time required to produce human-inspected cluster catalogs such as those used in the above study has limited the availability of star cluster samples. To greatly expand upon these samples, deep learning models have recently been proven capable of classifying star clusters at production-scale for nearby spiral galaxies (D < 20 Mpc). In order to optimize the reliability of such models, we use HST UV-optical imaging of over 20,000 objects from the PHANGS-HST survey to create updated models and investigate methods of improving their performance.

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