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Abstract Stripped-envelope supernovae (SESNe) represent a significant fraction of core-collapse supernovae, arising from massive stars that have shed their hydrogen and, in some cases, helium envelopes. The origins and explosion mechanisms of SESNe remain a topic of active investigation. In this work, we employ radiative-transfer simulations to model the light curves and spectra of a set of explosions of single, solar-metallicity, massive Wolf–Rayet stars with ejecta masses ranging from 4 to 11M_⊙, which were computed from a turbulence-aided and neutrino-driven explosion mechanism. We analyze these synthetic observables to explore the impact of varying ejecta mass and helium content on observable features. We find that the light curve shape of these progenitors with high ejecta masses is consistent with observed SESNe with broad light curves but not the peak luminosities. The commonly used analytic formula based on rising bolometric light curves overestimates the ejecta mass of these high-initial-mass progenitor explosions by a factor of up to 2.6. In contrast, the calibrated method by Haynie et al., which relies on late-time decay tails, reduces uncertainties to an average of 20% within the calibrated ejecta mass range. Spectroscopically, the He i1.083μm line remains prominent even in models with as little as 0.02M_⊙of helium. However, the strength of the optical He ilines is not directly proportional to the helium mass but instead depends on a complex interplay of factors such as the⁵⁶Ni distribution, composition, and radiation field. Thus, producing realistic helium features requires detailed radiative transfer simulations for each new hydrodynamic model. 

Abstract Nebular-phase observations of peculiar Type Ia supernovae (SNe Ia) provide important constraints on progenitor scenarios and explosion dynamics for both these rare SNe and the more common, cosmologically useful SNe Ia. We present observations from an extensive ground- and space-based follow-up campaign to characterize SN 2022pul, a super-Chandrasekhar mass SN Ia (alternatively “03fg-like” SN), from before peak brightness to well into the nebular phase across optical to mid-infrared (MIR) wavelengths. The early rise of the light curve is atypical, exhibiting two distinct components, consistent with SN Ia ejecta interacting with dense carbon–oxygen (C/O)-rich circumstellar material (CSM). In the optical, SN 2022pul is most similar to SN 2012dn, having a low estimated peak luminosity (M_B= −18.9 mag) and high photospheric velocity relative to other 03fg-like SNe. In the nebular phase, SN 2022pul adds to the increasing diversity of the 03fg-like subclass. From 168 to 336 days after peakB-band brightness, SN 2022pul exhibits asymmetric and narrow emission from [Oi]λλ6300, 6364 (FWHM ≈ 2000 km s⁻¹), strong, broad emission from [Caii]λλ7291, 7323 (FWHM ≈ 7300 km s⁻¹), and a rapid Feiiito Feiiionization change. Finally, we present the first ever optical-to-MIR nebular spectrum of an 03fg-like SN Ia using data from JWST. In the MIR, strong lines of neon and argon, weak emission from stable nickel, and strong thermal dust emission (withT≈ 500 K), combined with prominent [Oi] in the optical, suggest that SN 2022pul was produced by a white dwarf merger within C/O-rich CSM. 

Recent observations have found a growing number of hypervelocity stars with speeds of ≈1500 − 2500 km s⁻¹that could have only been produced through thermonuclear supernovae in white dwarf binaries. Most of the observed hypervelocity runaways in this class display a surprising inflated structure: their current radii are roughly an order of magnitude greater than they would have been as white dwarfs filling their Roche lobe. While many simulations exist studying the dynamical phase leading to supernova detonation in these systems, no detailed calculations of the long-term structure of the runaways have yet been performed. We used an existing AREPOhydrodynamical simulation of a supernova in a white dwarf binary as a starting point for the evolution of these stars with the one-dimensional stellar evolution code MESA. We show that the supernova shock is not energetic enough to inflate the white dwarf over timescales longer than a few thousand years, significantly shorter than the 10^5 − 6year lifetimes inferred for observed hypervelocity runaways. Although they experience a shock from a supernova less than ≈0.02 R_⊙away, our models do not experience significant interior heating, and all contract back to radii of around 0.01 R_⊙within about 10⁴years. Explaining the observed inflated states requires either an additional source of significant heating or some other physics that is not yet accounted for in the subsequent evolution. 

Many astrophysical applications require efficient yet reliable forecasts of stellar evolution tracks. One example is population synthesis, which generates forward predictions of models for comparison with observations. The majority of state-of-the-art rapid population synthesis methods are based on analytic fitting formulae to stellar evolution tracks that are computationally cheap to sample statistically over a continuous parameter range. The computational costs of running detailed stellar evolution codes, such as MESA, over wide and densely sampled parameter grids are prohibitive, while stellar-age based interpolation in-between sparsely sampled grid points leads to intolerably large systematic prediction errors. In this work, we provide two solutions for automated interpolation methods that offer satisfactory trade-off points between cost-efficiency and accuracy. We construct a timescale-adapted evolutionary coordinate and use it in a two-step interpolation scheme that traces the evolution of stars from zero age main sequence all the way to the end of core helium burning while covering a mass range from 0.65 to 300M_⊙. The feedforward neural network regression model (first solution) that we train to predict stellar surface variables can make millions of predictions, sufficiently accurate over the entire parameter space, within tens of seconds on a 4-core CPU. The hierarchical nearest-neighbor interpolation algorithm (second solution) that we hard-code to the same end achieves even higher predictive accuracy, the same algorithm remains applicable to all stellar variables evolved over time, but it is two orders of magnitude slower. Our methodological framework is demonstrated to work on the MESA ISOCHRONES ANDSTELLARTRACKS(Choi et al. 2016) data set, but is independent of the input stellar catalog. Finally, we discuss the prospective applications of these methods and provide guidelines for generalizing them to higher dimensional parameter spaces.