Boosting Algorithmic Efficiency: Numerical Relativity in Dynamical, Curvilinear Coordinates

提高算法效率：动态曲线坐标中的数值相对论

• 批准号: 1806596
• 负责人: Zachariah Etienne
• 金额: $ 15.59万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1806596&HistoricalAwards=false
• 关键词: Boosting; Algorithmic; Efficiency; Numerical; Relativity

Einstein's theory of general relativity (GR) provides science's current best understanding of gravity. It predicts the existence of bizarre objects like black holes and neutron stars, and ripples in spacetime called gravitational waves. These predictions motivated the construction of the Laser Interferometer Gravitational-wave Observatory (LIGO), which has detected several gravitational wave signals from colliding black holes and one signal from colliding neutron stars so far. For their efforts in making these detections possible, the leaders of LIGO were awarded the 2017 Nobel Prize in Physics. To obtain a deeper understanding about what produced the observed gravitational waves, LIGO data analysis compares observed waves with those predicted by Einstein's theory of GR for a very large number of possible scenarios. Construction of reliable theoretical models requires the full solutions to the equations underlying GR, provided by the field of numerical relativity. This project builds upon recent advances in this field to develop new software that greatly reduces the cost (in memory) of generating these solutions. Where before numerical relativity largely depended upon supercomputers, this new software will enable even consumer-grade desktop computers to generate needed theoretical predictions of merging black holes needed for LIGO data analysis. In the latter half of the funding period, the software will be made supercomputer-capable to enable (the far more memory-hungry) simulations of merging neutron stars on supercomputers with state-of-the-art accuracy. By unlocking the consumer-grade desktop as a powerful tool for numerical relativity, this project has the potential to enable the public to participate in the science in unprecedented ways. This will be made possible by incorporating this software into SETI@Home's "BOINC" volunteer computing infrastructure. The hope is that when LIGO detects a pair of merging black holes, thousands of black hole merger calculations will be launched for the benefit of LIGO science, each on a consumer-grade desktop computer running our "BlackHoles@Home" software. To educate the public and advertise this volunteer computing project both locally and globally, the West Virginia University group will give convocations in nearby high schools and write articles for the widely aggregated news site "The Conversation".Numerical relativity (NR) solves Einstein's equations of general relativity (GR), in full, on the computer. Improvements to the algorithmic and mathematical underpinnings of NR codes have recently culminated in a coming-of-age for the field, moving it beyond proof-of-principle calculations and into the realm of predictive astrophysics. Over the past two years, NR-based theoretical predictions of gravitational waves (GWs) were central to uncovering the binary parameters in LIGO's recent GW discoveries. Now that the age of multi-messenger astrophysics has arrived, physical scenarios involving gravitational field and magnetized fluid dynamics spanning orders of magnitude in length scale and timescale will need to be modeled. NR codes bridge these scales by dynamically adjusting their spatial numerical grids to better sample the space, but current algorithms do not account for near-symmetries in these systems or rely on complex mesh algorithms. The proposed project involves the development of a new NR code with the unique goal of being both algorithmically simple and highly efficient, minimizing computational and human effort while maximizing scientific impact. We call it SENR, the Simple, Efficient NR code. SENR is unique in its aim to perform NR simulations of compact binary inspirals atop a single, dynamical, bispherical-like spatial grid. Exploiting near-symmetries in the underlying system can reduce computational cost over the most widely-adopted NR methods by orders of magnitude, and minimizing the grid management infrastructure greatly simplifies the interpretation of numerical errors and the addition of new physics modules. SENR builds on a new, highly-robust approach in NR for solving the GR field equations with hydrodynamics on static curvilinear spatial grids with coordinate singularities (e.g., spherical polar coordinates), and the PI's team is extending the approach to arbitrary, dynamical coordinate systems. Development of SENR is accelerated by a Python-based code-generation tool developed for this project called NRPy+. Following best-practices in software design, the small SENR codebase is carefully optimized after each major feature is added to maximize scientific impact. SENR's memory efficiency unlocks the desktop as a tool for NR, enabling us to launch the first major volunteer computing effort to generate an enormous NR-based GW catalog for binary black holes. Further, SENR's scalability will enable us to leverage supercomputing resources to generate a very large double neutron star GW catalog as well. Simplicity in infrastructure greatly reduces effort required to add new physics modules, and we plan to incorporate Monte-Carlo-based photon and neutrino feedback to enable state-of-the-art realism in our compact binary simulations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

爱因斯坦的广义相对论（GR）提供了科学目前对引力的最佳理解。它预言了像黑洞和中子星这样的奇异物体的存在，以及时空中被称为引力波的涟漪。这些预测激发了激光干涉引力波天文台（LIGO）的建设，该天文台迄今已探测到来自碰撞黑洞的几个引力波信号和来自碰撞中子星的一个信号。由于他们在使这些探测成为可能方面所做的努力，LIGO的领导人被授予2017年诺贝尔物理学奖。为了更深入地了解是什么产生了观测到的引力波，LIGO数据分析将观测到的引力波与爱因斯坦的GR理论预测的引力波进行了大量可能的场景比较。可靠的理论模型的建设，需要完整的解决方案，提供的数值相对论领域的GR基本方程。这个项目建立在这个领域的最新进展，开发新的软件，大大降低了成本（在内存中）生成这些解决方案。以前数值相对论在很大程度上依赖于超级计算机，这种新软件将使消费级台式计算机能够生成LIGO数据分析所需的黑洞合并理论预测。在资助期的后半段，该软件将具备超级计算机的能力，以实现（更大的内存需求）在超级计算机上以最先进的精度模拟合并中子星。通过解锁消费级桌面作为数值相对论的强大工具，该项目有可能使公众以前所未有的方式参与科学。这将通过将该软件纳入SETI@Home的“BOINC”志愿者计算基础设施而成为可能。希望当LIGO探测到一对合并的黑洞时，为了LIGO科学的利益，将启动数千个黑洞合并计算，每个计算都在运行我们的“BlackHoles@Home”软件的消费级台式计算机上进行。为了教育公众并在当地和全球宣传这个志愿者计算项目，西弗吉尼亚大学的小组将在附近的高中举行集会，并为广泛聚合的新闻网站“对话”撰写文章。数值相对论（NR）在计算机上完整地解决了爱因斯坦的广义相对论方程（GR）。NR代码的算法和数学基础的改进最近在该领域的成熟中达到了高潮，使其超越了原理证明计算，进入了预测天体物理学领域。在过去的两年里，基于NR的引力波（GW）理论预测是揭示LIGO最近GW发现中的二元参数的核心。现在，多信使天体物理学的时代已经到来，涉及引力场和磁化流体动力学的物理场景在长度尺度和时间尺度上跨越了几个数量级。NR代码通过动态调整其空间数值网格来桥接这些尺度，以更好地对空间进行采样，但当前算法不考虑这些系统中的近对称性或依赖于复杂的网格算法。拟议的项目涉及开发一种新的NR代码，其独特的目标是算法简单且高效，最大限度地减少计算和人力投入，同时最大限度地提高科学影响。我们称之为SENR，简单高效的NR代码。SENR的独特之处在于它的目标是在一个单一的、动态的、类似双球面的空间网格上对紧凑的二元吸气进行NR模拟。利用底层系统中的近对称性可以将最广泛采用的NR方法的计算成本降低几个数量级，并且最小化网格管理基础设施大大简化了数值错误的解释和新物理模块的添加。SENR建立在NR中的一种新的、高度鲁棒的方法之上，用于在具有坐标奇异性的静态曲线空间网格上用流体动力学求解GR场方程（例如，球极坐标），PI的团队正在将该方法扩展到任意动态坐标系。为该项目开发的基于Python的代码生成工具NRPy+加速了SENR的开发。遵循软件设计的最佳实践，在添加每个主要功能后，小型SENR代码库都经过精心优化，以最大限度地发挥科学影响。SENR的内存效率解锁了桌面作为NR的工具，使我们能够启动第一个主要的志愿者计算工作，为二元黑洞生成一个巨大的基于NR的GW目录。此外，SENR的可扩展性将使我们能够利用超级计算资源来生成一个非常大的双中子星星GW目录。基础设施的简单性大大减少了添加新物理模块所需的工作量，我们计划采用基于蒙特-卡罗的光子和中微子反馈，以使我们的紧凑型二元模拟具有最先进的真实性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Addition of tabulated equation of state and neutrino leakage support to illinoisgrmhd

• DOI: 10.1103/physrevd.107.044037
• 发表时间: 2022-08
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Leonardo R. Werneck;Z. Etienne;A. Murguia-Berthier;R. Haas;F. Cipolletta;S. Noble;Lorenzo Ennoggi

Initial data and eccentricity reduction toolkit for binary black hole numerical relativity waveforms

• DOI: 10.1088/1361-6382/abe691
• 发表时间: 2020-11
• 期刊: Classical and Quantum Gravity
• 影响因子: 3.5
• 作者: Sarah Habib;A. Ramos-Buades;Eliu Huerta;S. Husa;R. Haas;Z. Etienne

Handing off the outcome of binary neutron star mergers for accurate and long-term postmerger simulations

• DOI: 10.1103/physrevd.106.083015
• 发表时间: 2021-12
• 期刊: Physical Review D
• 影响因子: 5
• 作者: F. L. Lopez Armengol;Z. Etienne;S. Noble;B. Kelly;Leonardo R. Werneck;B. Drachler;M. Campanelli

Fast hyperbolic relaxation elliptic solver for numerical relativity: Conformally flat, binary puncture initial data

用于数值相对论的快速双曲松弛椭圆求解器：共形平坦、二元穿刺初始数据

• DOI: 10.1103/physrevd.105.104037
• 发表时间: 2022
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Assumpção, Thiago;Werneck, Leonardo R.;Pierre Jacques, Terrence;Etienne, Zachariah B.
• 通讯作者: Etienne, Zachariah B.