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arXiv 1902.01340astro-ph.HE
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一维模拟Turbulence辅助的中微子驱动的核坍缩超新星爆炸

Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova Explosions in One Dimension

Sean M. Couch, MacKenzie L. Warren, Evan P. O'Connor

摘要:

核心坍缩超新星(CCSN)机制基本上是三维的,不稳定性,对流和湍流在帮助中微子驱动的爆炸中发挥着至关重要的作用。CCNSe的模拟包括中微子输运的精确处理和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,禁止在2D和3D中进行大参数研究。在1D中,跨越CCSNe令人难以置信的各种初始条件的大范围研究是可能的,尽管这种模拟必须人工驱动才能爆炸。我们提出了一种新的方法,包括中微子驱动的CCSNe的一维模拟中最重要的对流和湍流的影响,称为超新星湍流降维,或STIR。我们的新方法包括从湍流和对流运动的流动所产生的关键条款。我们估计的对流和湍流的强度,采用改进的混合长度理论(MLT)的方法,引入了一些自由参数的模型,适合的三维模拟的结果。对于足够大的值的混合长度参数,双折射辅助中微子驱动的爆炸。我们比较了STIR的结果,高保真的三维模拟,并进行了CCSN爆炸的参数研究,使用200个太阳金属丰度祖模型从9到120 $M_\odot$。我们发现,STIR是一个更好的预测模型将爆炸在多维模拟比其他方法驾驶爆炸在1D。我们还提出了一个初步的调查预测可观测的特点CCSN人口从STIR,如爆炸能量和残余质量的分布。

英文摘要:

The core-collapse supernova (CCSN) mechanism is fundamentally three-dimensional 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 amongst 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 (MLT) approach introducing a few free parameters to the model which 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 $M_\odot$. 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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