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Strong lensing offers a precious opportunity for studying the formation and early evolution of super star clusters that are rare in our cosmic backyard. The Sunburst Arc, a lensed Cosmic Noon galaxy, hosts a young super star cluster with escaping Lyman continuum radiation. Analyzing archival Hubble Space Telescope images and emission line data from Very Large Telescope/MUSE and X-shooter, we construct a physical model for the cluster and its surrounding photoionized nebula. We confirm that the cluster is ≲4 Myr old, is extremely massiveM_⋆∼ 10⁷M_⊙, and yet has a central component as compact as several parsecs, and we find a gas-phase metallicityZ= (0.22 ± 0.03)Z_⊙. The cluster is surrounded by ≳10⁵M_⊙of dense clouds that have been pressurized toP∼ 10⁹K cm⁻³by perhaps stellar radiation at within 10 pc. These should have large neutral columnsN_HI> 10^22.8cm⁻²to survive rapid ejection by radiation pressure. The clouds are likely dusty as they show gas-phase depletion of silicon, and may be conducive to secondary star formation ifN_HI> 10²⁴cm⁻²or if they sink farther toward the cluster center. Detecting strong [Niii]λλ1750,1752, we infer heavy nitrogen enrichment$\log (\mathrm{N}/\mathrm{O})=-{0.21}_{-0.11}^{+0.10}$. This requires efficiently retaining ≳500M_⊙of nitrogen in the high-pressure clouds from massive stars heavier than 60M_⊙up to 4 Myr. We suggest a physical origin of the high-pressure clouds from partial or complete condensation of slow massive star ejecta, which may have an important implication for the puzzle of multiple stellar populations in globular clusters.

 

Abstract Observations indicate that turbulent motions are present on most massive star surfaces. Starting from the observed phenomena of spectral lines with widths that are much larger than their thermal broadening (e.g., micro- and macroturbulence), and considering the detection of stochastic low-frequency variability (SLFV) in the Transiting Exoplanet Survey Satellite photometry, these stars clearly have large-scale turbulent motions on their surfaces. The cause of this turbulence is debated, with near-surface convection zones, core internal gravity waves, and wind variability being proposed. Our 3D gray radiation hydrodynamic (RHD) models previously characterized the convective dynamics of the surfaces, driven by near-surface convection zones, and provided reasonable matches to the observed SLFV of the most luminous massive stars. We now explore the complex emitting surfaces of these 3D RHD models, which strongly violate the 1D assumption of a plane-parallel atmosphere. By post-processing the gray RHD models with the Monte Carlo radiation transport code Sedona , we synthesize stellar spectra and extract information from the broadening of individual photospheric lines. The use of Sedona enables the calculation of the viewing angle and temporal dependence of spectral absorption line profiles. By combining uncorrelated temporal snapshots together, we compare the turbulent broadening from the 3D RHD models to the thermal broadening of the extended emitting region, showing that our synthesized spectral lines closely resemble the observed macroturbulent broadening from similarly luminous stars. More generally, the new techniques that we have developed will allow for systematic studies of the origins of turbulent velocity broadening from any future 3D simulations. 

Abstract Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the explosion outcome of core-collapse supernovae using a machine-learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with F ornax , we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9−27 M ⊙ , we additionally train an autoencoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and autoencoder features predict the explosion outcome with ≈90% accuracy. In anticipation of much larger multidimensional simulation sets, we identify future directions in which machine-learning applications will be useful beyond the explosion outcome prediction.