Speeding Up the Spinning, Precessing Effective One-Body--Numerical Relativity (SEOBNRv3) Code by ~10,000x

当LIGO探测到引力波时，LIGO科学合作组织（LSC）的一组数据分析人员通过使用一套工具来估计探测到的信号的源参数，来解决“究竟是什么产生了这种引力波”的问题。他们的发现扩展了我们对宇宙的理解，并激励了下一代科学家。参数估计的过程需要将观测到的引力波与理论模型产生的数百万个引力波进行比较。然而，这带来了一个重大挑战：目前用于这项任务的软件速度太慢，需要长达1000年的时间来估计单个探测到的引力波的参数。这笔拨款将支持将该软件平台性能提高10,000倍的工作，这将大大增加这种最先进和广泛使用的LSC参数估计模型的实用性。该拨款还将支持LSC自动化软件验证系统的实施，以防止某些类型的软件“错误”影响参数估计。对于那些感兴趣的来源是旋转黑洞对的情况，LSC主要使用两个高度可靠的理论模型，这些模型基于超级计算机生成的爱因斯坦广义相对论方程的解。其中一个模型被称为SEOBNRv3 （spin Effective One Body-Numerical Relativity model， version 3的缩写）。SEOBNR系列引力波形模型是目前用于自旋黑洞双子星参数估计（PE）的最佳模型之一，用数值相对论模拟的结果填补了摄动波形近似的空白。尽管与数值相关性波形相比，SEOBNR编码具有很高的效率和可靠性，但对于基于标准马尔可夫链蒙特卡罗（MCMC）的PE来说，SEOBNR编码仍然太慢，无法直接使用。该奖项支持大幅提高官方SEOBNRv3 （v3）软件性能的工作。除此之外，该奖项还将支持STEM研究领域的研究生教育。

When LIGO detects a gravitational wave, a group of data analysts within the LIGO Scientific Collaboration (LSC) addresses the question "What exactly produced this wave?", by employing a suite of tools designed to estimate source parameters of detected signals. Their findings extend our understanding of the universe, and inspire the next generation of scientists. The process of parameter estimation requires that the observed wave be compared to many millions of gravitational waves generated by theoretical models. However, this poses a major challenge: the software currently used for this task is far too slow, requiring up to 1,000 years to estimate parameters for a single detected gravitational wave. This grant will support an effort to improve this software platform performance by a factor of 10,000, which will greatly increase the usefulness of this state-of-the-art and widely-used model for LSC parameter estimation. The grant will also support the implementation of an automated software validation system for the LSC, to prevent certain types of software "bugs" from influencing parameter estimation.For cases where the sources of interest are spinning black-hole pairs, the LSC primarily makes use of two highly-reliable theoretical models based on supercomputer-generated solutions to Einstein's equations of General Relativity. One of these models is called SEOBNRv3 (short for Spinning Effective One Body-Numerical Relativity model, version 3). The SEOBNR series of gravitational waveform models are among the best available for parameter estimation (PE) of spinning black-hole binaries, filling gaps in perturbative waveform approximants with results from numerical relativity simulations. Despite their great efficiency and reliability when compared to numerical relativity waveforms, SEOBNR codes are still far too slow to be directly useful for standard Markov-Chain Monte Carlo (MCMC)-based PE. This award supports work to drastically improve the performance of the official SEOBNRv3 (v3) software. In addition to this effort, the award will support the education of graduate students in STEM areas of research.