Higher-Mach-number self-similar shock solutions in failed supernovae are unstable and strengthen asymptotically above a critical neutrino mass-loss threshold, explaining greater ejection in red supergiants versus compact progenitors.
W., Hempel, M., & Fischer, T
8 Pith papers cite this work, alongside 733 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
libyt provides a bidirectional C-Python interface for in-situ analysis of patch-based AMR simulations using yt and Jupyter with minimal workflow changes.
The authors implement and validate nuclear networks coupled to GRRMHD with M1 neutrino transport in Gmunu, showing effects on core-collapse supernova shock revival and composition.
Multi-D progenitor perturbations leave shock revival and explosion energetics essentially unchanged in 2D CHIMERA runs; any effects sit below the level of numerical stochasticity.
For a low-mass CCSN progenitor, rotation alone weakly and non-monotonically affects explosion energy and observables; only the fastest spin yields T/|W| spiral modes and spin-kick alignment, with core spin amplified by ~4000.
New effective potentials derived from projections of the Einstein equations approximate GR corrections in Newtonian core-collapse supernova simulations and match full GR results closely.
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
Neutron star observations, especially the heaviest known pulsar masses and GW170817 tidal deformability, provide the strongest restrictions on the allowed cold dense matter equation of state.
citing papers explorer
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On the Origin of Mass Ejection in Failed Supernovae
Higher-Mach-number self-similar shock solutions in failed supernovae are unstable and strengthen asymptotically above a critical neutrino mass-loss threshold, explaining greater ejection in red supergiants versus compact progenitors.
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libyt: an In Situ Interface Connecting Simulations with yt, Python, and Jupyter Workflows
libyt provides a bidirectional C-Python interface for in-situ analysis of patch-based AMR simulations using yt and Jupyter with minimal workflow changes.
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Toward First-Principles Multi-Messenger Predictions: Coupling Nuclear Networks with GR Radiation-MHD in {\tt Gmunu}
The authors implement and validate nuclear networks coupled to GRRMHD with M1 neutrino transport in Gmunu, showing effects on core-collapse supernova shock revival and composition.
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Impacts of Multidimensional Progenitor Perturbations on Core-Collapse Supernova Explosions
Multi-D progenitor perturbations leave shock revival and explosion energetics essentially unchanged in 2D CHIMERA runs; any effects sit below the level of numerical stochasticity.
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Effects of Rotation on 3D Core-Collapse Supernova Models for Low-Mass Progenitors
For a low-mass CCSN progenitor, rotation alone weakly and non-monotonically affects explosion energy and observables; only the fastest spin yields T/|W| spiral modes and spin-kick alignment, with core spin amplified by ~4000.
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Approximating General Relativity in Core-Collapse Supernova Simulations
New effective potentials derived from projections of the Einstein equations approximate GR corrections in Newtonian core-collapse supernova simulations and match full GR results closely.
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Reconstruction of fast-rotating neutron star observables with the neural network
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
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Astrophysical constraints on the cold equation of state of the strongly interacting matter
Neutron star observations, especially the heaviest known pulsar masses and GW170817 tidal deformability, provide the strongest restrictions on the allowed cold dense matter equation of state.