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

将旋转、进动有效一体数值相对论 (SEOBNRv3) 代码加速约 10,000 倍

• 批准号: 1607405
• 负责人: Zachariah Etienne
• 金额: $ 9.9万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607405&HistoricalAwards=false
• 关键词: Speeding; Up; Spinning; Precessing; Effective

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.

当LIGO探测到引力波时，LIGO科学合作组织（LSC）的一组数据分析师提出了一个问题：“到底是什么产生了这种波？”“，通过采用一套设计用于估计检测信号的源参数的工具。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1,000年的时间来估计单个探测到的引力波的参数。这笔赠款将支持将该软件平台的性能提高10，000倍的努力，这将大大提高这种最先进和广泛使用的模型用于LSC参数估计的实用性。该拨款还将支持为LSC实施一个自动软件验证系统，以防止某些类型的软件“错误”影响参数估计。对于感兴趣的源是旋转黑洞对的情况，LSC主要利用两个高度可靠的理论模型，这些模型基于爱因斯坦广义相对论方程的超级计算机生成的解。其中一个模型被称为SEOBNRv 3（自旋有效一体-数值相对论模型，第3版）。SEOBNR系列引力波形模型是旋转黑洞双星参数估计（PE）的最佳模型之一，填补了微扰波形近似与数值相对论模拟结果的空白。尽管与数值相关性波形相比，SEOBNR代码具有很高的效率和可靠性，但它仍然太慢，无法直接用于基于标准马尔可夫链蒙特卡罗（MCMC）的PE。该奖项支持大幅提高官方SEOBNRv 3（v3）软件性能的工作。除了这一努力，该奖项将支持研究生在STEM研究领域的教育。