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: 2110352
• 负责人: Zachariah Etienne
• 金额: $ 17.48万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110352&HistoricalAwards=false
• 关键词: Boosting; Algorithmic; Efficiency; Numerical; Relativity

This award supports research in relativity and relativistic astrophysics and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. 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 NSF's Laser Interferometer Gravitational-wave Observatory (LIGO), which has detected several gravitational wave signals from colliding black holes and neutron stars over the past years. For their efforts in making these detections possible, the leaders of LIGO were awarded the 2017 Nobel Prize in Physics. Much of gravitational wave (GW) science depends on GW observations being compared with millions of theoretical predictions, which must be built upon GW catalogs extracted from numerical relativity (NR) simulations. NR simulations solve the GR equations in full on the computer, and to date each of these NR simulations has required a small computing cluster, which has limited throughput to only about 3,000 GWs in 15 years. Given the vast number of possible scenarios for even the simplest and most commonly observed GW source, binary black holes (BBHs), such a small GW collection threatens potential science gains from future GW observations. BlackHoles@Home is a proposed citizen-science project leveraging new techniques to fit NR BBH simulations on a consumer-grade desktop computer, enabling new GW catalog generation with unprecedented throughput using volunteer computers. Such throughput will enable far more detailed analyses of observed GWs from current and future GW detectors, maximizing the science gained from hard-fought observations. To educate the public and advertise this volunteer computing project both locally and globally, convocations will be given in underserved high schools, and updates will be posted to a widely disseminated BlackHoles@Home email newsletter.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 six years, NR-based theoretical predictions of gravitational waves (GWs) were central to uncovering the binary parameters in LIGO and Virgo's recent GW discoveries. Looking ahead, GW catalogs generated by NR simulations of compact binaries will need to grow greatly to ensure that parameter estimation accuracy can keep up with increased sensitivity of GW interferometers. BlackHoles@Home is a proposed BOINC project that aims to fit binary black hole (BBH) simulations on the consumer-grade desktop computer. In doing so the general public can be enlisted to help generate the large GW catalogs that form the foundation for a great deal of GW science. Traditionally, these BBH simulations have been performed on supercomputers. BlackHoles@Home implements new approaches for robustly solving Einstein's equations of general relativity in highly efficient coordinate systems, so that these simulations will fit on consumer-grade desktop computers in only a few gigabytes of RAM. BlackHoles@Home's core infrastructure provides a firm foundation for compact binary simulations beyond BBHs. To this end, the dynamical-spacetime GRMHD code IllinoisGRMHD will be incorporated into this infrastructure to enable state-of-the-art binary neutron star simulations on supercomputers. These simulations will leverage both recent advances in solving the GRMHD equations in spherical-like coordinate systems, as well as recent improvements to IllinoisGRMHD that add both advanced nuclear equation of state support and basic neutrino physics. Monte-Carlo-based photon and neutrino feedback will also be incorporated to enable state-of-the-art realism in these binary neutron star 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.

该奖项支持相对论和相对论天体物理学的研究，并阐述了美国国家科学基金会“宇宙之窗”宏伟构想的优先领域。爱因斯坦的广义相对论(GR)提供了目前科学上对引力的最好理解。它预测了黑洞和中子星等奇异物体的存在，以及被称为引力波的时空涟漪的存在。这些预测推动了NSF激光干涉仪引力波天文台(LIGO)的建设，在过去的几年里，LIGO探测到了几个来自黑洞和中子星碰撞的引力波信号。由于他们为实现这些探测所做的努力，LIGO的领导人被授予2017年诺贝尔物理学奖。引力波(GW)科学的很大一部分依赖于GW观测与数百万理论预测的比较，这些理论预测必须建立在从数值相对论(NR)模拟中提取的GW星表的基础上。NR模拟在计算机上完整地求解GR方程，到目前为止，每个NR模拟都需要一个小的计算集群，其吞吐量在15年内仅限于约3000 GW。考虑到即使是最简单和最常见的GW源--双星黑洞(BBH)也有大量可能的情况，如此小的GW收集威胁到未来GW观测的潜在科学成果。Blackholes@Home是一个拟议的公民科学项目，利用新技术在消费级台式计算机上进行NR BBH模拟，使用志愿者计算机以前所未有的吞吐量生成新的GW目录。这样的吞吐量将使目前和未来的GW探测器对观测到的GW进行更详细的分析，最大限度地发挥从艰苦观测中获得的科学成果。为了教育公众并在本地和全球范围内宣传这一志愿者计算项目，将在服务不足的高中举行集会，并将更新发布到广泛传播的BlackHoles@Home电子邮件通讯中。对NR代码算法和数学基础的改进最近在该领域的成熟中达到顶峰，使其超越了原理证明计算，进入预测天体物理学领域。在过去的六年里，基于NR的引力波(GW)理论预测是揭示LIGO和Virgo最近GW发现中的二元参数的核心。展望未来，由致密双星NR模拟生成的GW星表将需要大幅增长，以确保参数估计精度能够跟上GW干涉仪提高的灵敏度。Blackholes@Home是BOINC提出的一个项目，旨在适应消费级台式计算机上的二进制黑洞(BBH)模拟。通过这样做，普通公众可以被招募来帮助生成大型全球水资源目录，这些目录构成了大量全球水资源科学的基础。传统上，这些BBH模拟是在超级计算机上进行的。Blackholes@Home实现了在高效坐标系中强有力地求解爱因斯坦广义相对论方程的新方法，因此这些模拟只需几GB的RAM就可以在消费级台式计算机上进行。Blackholes@Home的核心基础设施为超越BBH的紧凑型二进制模拟提供了坚实的基础。为此，动力学时空GRMHD代码IllinoisGRMHD将被纳入到这个基础设施中，以实现在超级计算机上进行最先进的双中子星模拟。这些模拟将利用在类球面坐标系中求解GRMHD方程的最新进展，以及最近对IllinoisGRMHD的改进，该改进增加了高级核状态方程支持和基本中微子物理。蒙特卡洛的光子和中微子反馈也将被纳入这些双中子星模拟中，以实现最先进的真实感。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

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.