Convection-aided Explosions in One-dimensional Core-collapse Supernova Simulations. I. Technique and Validation
Most one-dimensional core-collapse simulations fail to explode, yet multidimensional simulations often explode. A dominant multidimensional effect aiding explosion is neutrino-driven convection. We incorporate a convection model in approximate one-dimensional core-collapse supernova (CCSN) simulatio...
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Veröffentlicht in: | The Astrophysical journal 2019-12, Vol.887 (1), p.43 |
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Sprache: | eng |
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Zusammenfassung: | Most one-dimensional core-collapse simulations fail to explode, yet multidimensional simulations often explode. A dominant multidimensional effect aiding explosion is neutrino-driven convection. We incorporate a convection model in approximate one-dimensional core-collapse supernova (CCSN) simulations. This is the 1D+ method. This convection model lowers the neutrino luminosity required for explosion by %, similar to the reduction observed in multidimensional simulations. The model is based upon the global turbulence model of Mabanta & Murphy and models the mean-field turbulent flow of neutrino-driven convection. In this preliminary investigation, we use simple neutrino heating and cooling algorithms to compare the critical condition in the 1D+ simulations with the critical condition observed in two-dimensional simulations. Qualitatively, the critical conditions in the 1D+ and the two-dimensional simulations are similar. The assumptions in the convection model affect the radial profiles of density, entropy, and temperature, and comparisons with the profiles of three-dimensional simulations will help to calibrate these assumptions. These 1D+ simulations are consistent with the profiles and explosion conditions of equivalent two-dimensional CCSN simulations but are ∼102 times faster, and the 1D+ prescription has the potential to be ∼105 faster than three-dimensional CCSN simulations. With further calibration, the 1D+ technique could be ideally suited to test the explodability of thousands of progenitor models. |
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ISSN: | 0004-637X 1538-4357 1538-4357 |
DOI: | 10.3847/1538-4357/ab4bcc |