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Abstract In order to better connect core-collapse supernova (CCSN) theory with its observational signatures, we have developed a simulation pipeline from the onset of the core collapse to beyond shock breakout from the stellar envelope. Using this framework, we present a 3D simulation study from 5 s to over 5 days following the evolution of a 17M_⊙progenitor, exploding with ∼10⁵¹erg of energy and ∼0.1M_⊙of⁵⁶Ni ejecta. The early explosion is highly asymmetric, expanding most prominently along the southern hemisphere. This early asymmetry is preserved to shock breakout, ∼1 day later. Breakout itself evinces strong angle-dependence, with as much as 1 day delay in the shock breakout by direction. The nickel ejecta closely tail the forward shock, with velocities at the breakout as high as ∼7000 km s⁻¹. A delayed reverse shock forming at the H/He interface on hour timescales leads to the formation of Rayleigh–Taylor instabilities, fast-moving nickel bullets, and almost complete mixing of the metal core into the hydrogen envelope. For the first time, we illustrate the angle-dependent emergent broadband and bolometric light curves from simulations evolved in 3D in entirety, continuing through hydrodynamic shock breakout from a CCSN model of a massive stellar progenitor evolved with detailed, late-time neutrino microphysics and transport. Our case study of a single progenitor underscores that 3D simulations generically produce the cornucopia of observed asymmetries and features in CCSNe observations, while establishing the methodology to study this problem in breadth. 

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. 

Abstract Eruptive mass loss likely produces the energetic outbursts observed from some massive stars before they become core-collapse supernovae (SNe). The resulting dense circumstellar medium may also cause the subsequent SNe to be observed as Type IIn events. The leading hypothesis of the cause of these outbursts is the response of the envelope of the red supergiant (RSG) progenitor to energy deposition in the months to years prior to collapse. Early theoretical studies of this phenomenon were limited to 1D, leaving the 3D convective RSG structure unaddressed. UsingFLASH's hydrodynamic capabilities, we explore the 3D outcomes by constructing convective RSG envelope models and depositing energies less than the envelope binding energies on timescales shorter than the envelope dynamical time deep within them. We confirm the 1D prediction of an outward-moving acoustic pulse steepening into a shock, unbinding the outermost parts of the envelope. However, we find that the initial 2–4 km s⁻¹convective motions seed the intrinsic convective instability associated with the high-entropy material deep in the envelope, enabling gas from deep within the envelope to escape and increasing the amount of ejected mass compared to an initially “quiescent” envelope. The 3D models reveal a rich density structure, with column densities varying by ≈10× along different lines of sight. Our work highlights that the 3D convective nature of RSG envelopes impacts our ability to reliably predict the outburst dynamics, the amount, and the spatial distribution of the ejected mass associated with deep energy deposition.