Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova Explosions in One Dimension
Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova Explosions in One Dimension
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DOI：
10.3847/1538-4357/ab609e
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
S. Couch;M. Warren;E. O’Connor
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文献类型：
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作者：
S. Couch;M. Warren;E. O’Connor
核心塌缩超新星(CCSN)的机制基本上是3D的，不稳定、对流和湍流在帮助中微子驱动的爆炸中起着至关重要的作用。对CCNSe的模拟，包括对中微子输运的准确处理，以及足以捕捉关键不稳定性的足够分辨率，仍然是天体物理学中最昂贵的数值模拟之一，阻止了2D和3D的大参数研究。在CCSNe初始条件变化惊人的大范围内进行研究，可能是一维的，尽管这样的模拟必须人工驱动才能爆炸。我们提出了一种新的方法，在中微子驱动的CCSNe一维模拟中考虑对流和湍流的最重要影响，称为降维超新星湍流，或STIR。我们的新方法包括由水流的湍流和对流运动产生的关键项。我们使用改进的混合长度理论方法来估计对流和湍流的强度，在模型中引入了一些与三维模拟结果相吻合的自由参数。对于足够大的混合长度参数，可以得到湍流辅助的中微子驱动爆炸。我们将STIR的模拟结果与高保真的3D模拟结果进行了比较，并使用200个太阳金属丰度模型对CCSN爆炸进行了参数研究。我们发现，与其他驱动一维爆炸的方法相比，STIR能更好地预测哪些模型将在多维模拟中爆炸。我们还初步研究了从STIR中预测的CCSN群体的可观测特征，如爆炸能量和残余质量的分布。
The core-collapse supernova (CCSN) mechanism is fundamentally 3D, with instabilities, convection, and turbulence playing crucial roles in aiding neutrino-driven explosions. Simulations of CCNSe including accurate treatments of neutrino transport and sufficient resolution to capture key instabilities remain among the most expensive numerical simulations in astrophysics, prohibiting large parameter studies in 2D and 3D. Studies spanning a large swath of the incredibly varied initial conditions of CCSNe are possible in 1D, though such simulations must be artificially driven to explode. We present a new method for including the most important effects of convection and turbulence in 1D simulations of neutrino-driven CCSNe, called Supernova Turbulence In Reduced-dimensionality, or STIR. Our new approach includes crucial terms resulting from the turbulent and convective motions of the flow. We estimate the strength of convection and turbulence using a modified mixing-length theory approach, introducing a few free parameters to the model that are fit to the results of 3D simulations. For sufficiently large values of the mixing-length parameter, turbulence-aided neutrino-driven explosions are obtained. We compare the results of STIR to high-fidelity 3D simulations and perform a parameter study of CCSN explosion using 200 solar-metallicity progenitor models from 9 to 120 . We find that STIR is a better predictor of which models will explode in multidimensional simulations than other methods of driving explosions in 1D. We also present a preliminary investigation of predicted observable characteristics of the CCSN population from STIR, such as the distributions of explosion energies and remnant masses.