Collaborative Research: Sparse spectral-tau methods for binary neutron star initial data

合作研究：双中子星初始数据的稀疏谱tau方法

• 批准号: 1216866
• 负责人: Stephen Lau
• 金额: $ 13.15万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216866&HistoricalAwards=false
• 关键词: Collaborative; Research; Sparse; spectral; tau

Binary neutron star inspiral is the most certain source of gravitational waves detectable by Earth-based observatories like the US LIGO project, and simulations of such binaries should facilitate eventual detections. These simulations require initial conditions: solutions to the initial value problem of general relativity for the coupled gravity-matter system. The conformal thin sandwich method is an excellent approach for solving the initial value problem; however, although not an intrinsic assumption of the method, in practice the approach has assumed conformal flatness (as have other valuable approaches). Conformal flatness yields unphysical junk radiation. By numerically constructing helically symmetric solutions to the Einstein equations, the PI will extract initial data (or conformal thin sandwich trial data) which does not rely on conformal flatness, and therefore contains the correct initial gravitational wave content. The mixed PDEs arising from the helical reduction of the Einstein equations (or their approximation in the post-Minkowski formalism) will be solved with innovative techniques: sparse modal spectral-tau methods with new preconditioning strategies. In part, these strategies may rely on randomized algorithms for the interpolative decomposition. Spectral methods deliver superb accuracy for smooth problems(neutron star spacetimes are smooth almost everywhere), and sparsity affords a fast matrix-vector multiply when using a Krylov-subspace method to iteratively solve a linear system. Whereas the preconditioning of nodal (collocation) spectral methods is well studied, less is known about modal preconditioning. Our techniques have been successfully applied to models of the binary neutron star problem. Moreover, the problem's physical structure has already been explored with different, but limited, techniques. This project is to combine two sets of techniques (each already developed) and further develop the first set (spectral-tau methods), in order to obtain new results for a leading problem in gravitational wave physics. The PI will develop these mathematical methods by applying them to the specific neutron star problem described above. This strategy of specificity is often used in the development of techniques, which then prove to be more general. Because the scientific problem is of great interest, much is known about it, and results therefore exist withwhich comparisons can be made. These comparisons facilitate the development of mathematical algorithms. Conversely, new mathematical methods deliver more and/or better solutions which enhances scientific understanding.

双中子星星螺旋是最确定的引力波源检测地球上的天文台，如美国LIGO项目，和模拟这样的二进制应有助于最终的检测。这些模拟需要初始条件：引力-物质耦合系统的广义相对论初值问题的解。共形薄夹层法是求解初值问题的一种很好的方法;然而，虽然不是该方法的内在假设，但在实践中该方法假设了共形平坦性（如其他有价值的方法）。共形平坦度会产生非物理的垃圾辐射。通过数值构造爱因斯坦方程的螺旋对称解，PI将提取不依赖于共形平坦度的初始数据（或共形薄夹层试验数据），因此包含正确的初始引力波内容。混合偏微分方程所产生的螺旋减少爱因斯坦方程（或其近似的后闵可夫斯基形式主义）将解决创新技术：稀疏模态谱τ方法与新的预处理策略。部分地，这些策略可以依赖于用于插值分解的随机化算法。谱方法为光滑问题提供了极好的精度（中子星星时空几乎在任何地方都是光滑的），稀疏性在使用Krylov子空间方法迭代求解线性系统时提供了快速的矩阵向量乘法。而预处理的节点（配置）谱方法的研究是很好的，很少知道模态预处理。我们的技术已成功地应用于模型的双中子星星的问题。此外，问题的物理结构已经用不同但有限的技术进行了探索。该项目将联合收割机两套技术（每一套都已开发）结合起来，并进一步发展第一套技术（谱τ方法），以便为引力波物理学中的一个主要问题获得新的结果。PI将通过将这些数学方法应用于上述特定的中子星星问题来发展这些数学方法。这种特异性的策略经常被用于技术的开发，然后被证明是更普遍的。因为这个科学问题具有很大的意义，所以人们对它的了解很多，因此存在着可以用来进行比较的结果。这些比较促进了数学算法的发展。相反，新的数学方法提供了更多和/或更好的解决方案，从而增强了科学理解。