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arXiv · 2510.13741

Dynamics and observational signatures of core-collapse supernovae with central engines: hydrodynamics simulations with Monte Carlo post-processing

Abstract

A long-lived central engine embedded in expanding supernova ejecta can alter the dynamics and observational signatures of the event, producing an unusually luminous, energetic, and/or rapidly-evolving transient. We use two-dimensional hydrodynamics simulations to study the effect of a central energy source, varying the amount, rate, and isotropy of the energy deposition. We post-process the results with a time-dependent Monte Carlo radiation transport code to extract observational signatures. The engine excavates a bubble at the centre of the ejecta, which becomes Rayleigh-Taylor unstable. Sufficiently powerful engines are able to break through the edge of the bubble and accelerate, shred, and compositionally mix the entire ejecta. The breakout of the engine-driven wind occurs at distinct rupture points, and the outflowing high-velocity gas may eventually give rise to radio emission. The dynamical impact of the engine leads to faster rising optical light curves, with photon escape facilitated by the faster expansion of the ejecta and the opening of low-density channels. For models with strong engines, the spectra are initially hot and featureless, but later evolve to resemble those of broad-line Ic supernovae. Under certain conditions, line emission from ionized, low-velocity material near the centre of the ejecta may be able to escape and produce narrow emission similar to that seen in interacting supernovae. We discuss how variability in the engine energy reservoir and injection rate could give rise to a heterogeneous set of events spanning multiple observational classes, including the fast blue optical transients, broad-line Ic supernovae, and superluminous supernovae.

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BibTeXRIS

Kiran Eiden, Daniel Kasen. 2026-04-29. Dynamics and observational signatures of core-collapse supernovae with central engines: hydrodynamics simulations with Monte Carlo post-processing. https://doi.org/10.1093/mnras%2Fstag306

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