Stephen Hannon, UC Riverside

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

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 objects from the PHANGS-HST survey to create updated models and investigate methods of improving their performance. 

 

Abigail Frost, KU Leuven

Probing massive stars from young to old with optical/IR interferometry

Massive stars, those at least eight times the mass of our Sun, are very influential sources, affect not only their local environments but also their galaxies as a whole. Their luminosities dominate in spatially unresolved galaxies, and their winds are powerful to the point that they affect the topology of galactic superwinds (e.g. Leitherer 1994). Massive stars are also the originators of many exotic phenomena, from core-collapse supernovae (which enrich to interstellar medium) to neutron stars, black holes and their associated gravitational waves. Gravitational waves in particular are a result of the multiplicity of massive stars (Abbot et al. 2016) which has the potential to completely alter the evolutionary pathway of the involved stars if the stars interact. In this talk I will discuss some of the interesting massive multiple systems (including potential merger systems and stars stripped by interaction in binary systems) and how I probe the massive star formation process with IR interferometry.