Boosting Algorithmic Efficiency: Numerical Relativity in Dynamical, Curvilinear Coordinates

该奖项支持相对论和相对论天体物理学的研究，并解决了NSF“宇宙之窗”大理念的优先领域。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NSF的激光干涉引力波天文台（LIGO）的建设，该天文台在过去几年中探测到了来自碰撞黑洞和中子星的几个引力波信号。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。大部分引力波（GW）科学依赖于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目录上。NR模拟在计算机上完全求解GR方程，迄今为止，这些NR模拟中的每一个都需要一个小型计算集群，这将吞吐量限制在15年内仅约3，000 GW。考虑到即使是最简单和最常见的GW源，二元黑洞（BBH）的大量可能场景，这样一个小的GW收集威胁到未来GW观测的潜在科学收益。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。这样的吞吐量将能够对当前和未来的GW探测器观测到的GW进行更详细的分析，最大限度地提高从艰苦观测中获得的科学。为了教育公众并在本地和全球宣传这个志愿者计算项目，将在服务不足的高中举行会议，并将更新发布到广泛传播的BlackHoles@Home电子邮件通讯中。NR代码的算法和数学基础的改进最近达到了该领域成熟的高潮，使其超越了原理证明计算，进入了预测天体物理学的领域。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，由紧凑型双星的NR模拟生成的GW目录将需要大幅增长，以确保参数估计精度能够跟上GW干涉仪灵敏度的提高。BlackHoles@Home是BOINC提出的一个项目，旨在在消费级桌面计算机上进行二进制黑洞（BBH）模拟。在这样做的过程中，公众可以被招募来帮助生成大型GW目录，这些目录构成了大量GW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稳健地求解爱因斯坦广义相对论方程的新方法，因此这些模拟将适合仅几千兆字节RAM的消费级台式计算机。BlackHoles@Home的核心基础设施为BBH之外的紧凑型二元模拟提供了坚实的基础。为此，动态时空GRMHD代码IllinoisGRMHD将被纳入这一基础设施，使最先进的二元中子星星模拟的超级计算机。这些模拟将利用最近在类球坐标系中求解GRMHD方程的进展，以及最近对IllinoisGRMHD的改进，这些改进增加了先进的核状态方程支持和基本的中微子物理学。基于蒙特-卡罗的光子和中微子反馈也将被纳入这些二元中子星星模拟中，以实现最先进的现实主义。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。

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.