Improving the Accuracy, Robustness, and Computational Efficiency of the Spinning, Precessing Effective-One-Body Numerical-Relativity (SEOBNRv3/SEOBNRv4P) Codes

提高旋转、进动有效单体数值相对论 (SEOBNRv3/SEOBNRv4P) 代码的准确性、鲁棒性和计算效率

• 批准号: 1912497
• 负责人: Sean McWilliams
• 金额: $ 10万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912497&HistoricalAwards=false
• 关键词: Improving; Accuracy; Robustness; Computational; Efficiency

This award supports research in relativity and relativistic astrophysics and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. In order to detect and characterize the gravitational-wave signals observed by Advanced LIGO, theoretical models that predict the detailed behavior of the signal as a function of the source parameters are required. These models must provide an accurate approximation to the exact solution of Einstein's equations, but must also be rapidly and robustly calculable, so that they can be used to predict the signal at hundreds of millions of parameter locations in order to characterize a single event. To that end, this project will create a new state-of-the-art in accurate and efficient waveform models, thus helping to ensure that future Advanced LIGO observations are not limited by the waveform models being used. In addition to the scientific benefits, this work will result in the training of a graduate student in the best practices of analytical relativity and gravitational-wave data analysis, thereby helping to train the next generation of gravitational-wave astronomers. Finally, this proposal will help facilitate impactful education and outreach throughout the state of West Virginia, by contributing to the existing Space Public Outreach Team (SPOT) program through the development of a new presentation on gravitational-wave astrophysics.The Precessing Spinning Effective-One-Body Numerical-Relativity (SEOBNRv3, and SEOBNRv4P currently under development) gravitational waveform model is one of only two models (along with IMRPhenomP) capable of facilitating complete parameter estimation (PE) of black-hole binary events. However, despite their great efficiency and reliability when compared to numerical relativity waveforms, the original SEOBNR codes were still far too slow to be directly useful for standard Markov-Chain Monte Carlo (MCMC)-based PE. To address this issue, the PI's team previously developed optimized versions of the SEOBNR approximants, which make it possible, using SEOBNRv3_opt, to perform PE on a candidate event at a much faster rate. In addition, the development of SEOBNRv3_opt uncovered occasional pathological behavior in the underlying SEOBNRv3 approximant. While only occurring in one out of every ~10,000 to 100,000 cases, this frequency nonetheless presents a major obstacle to PE, which requires the generation of 10^8 waveform realizations. Therefore, there is an urgent need to develop an approximant that is both significantly more efficient than SEOBNRv3_opt, and also substantially more robust. This project will create a new state-of-the-art approximant that is both more accurate and more robust than any currently in existence. This will require, first, the creation of a new approximant based on SEOBNRv3_opt, but replacing the ringdown attachment with the Backwards One-Body (BOB) merger-ringdown model developed by the PI, which produces merger-ringdown waveforms as accurate as NR results across the entire range of parameter space probed by NR. Because BOB can be extended to earlier times than the light ring, it can avoid the extreme sensitivity of SEOBNRv3 to the exact light ring location, thereby dramatically improving the robustness of the model.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，并涉及美国国家科学基金会“宇宙之窗”大创意的优先领域。为了检测和表征高级 LIGO 观测到的引力波信号，需要预测信号作为源参数函数的详细行为的理论模型。这些模型必须提供爱因斯坦方程精确解的精确近似，但也必须能够快速、稳健地计算，以便它们可以用于预测数亿个参数位置的信号，从而表征单个事件。为此，该项目将创建准确高效的最新波形模型，从而有助于确保未来的高级 LIGO 观测不受所使用的波形模型的限制。除了科学效益之外，这项工作还将培训研究生分析相对论和引力波数据分析的最佳实践，从而帮助培训下一代引力波天文学家。最后，该提案将通过开发关于引力波天体物理学的新演示文稿，为现有的太空公共外展团队 (SPOT) 计划做出贡献，从而有助于促进整个西弗吉尼亚州的有影响力的教育和外展活动。进动旋转有效单体数值相对论（SEOBNRv3 和 SEOBNRv4P 目前正在开发）引力波形模型是仅有的两个模型之一（与 IRPhenomP 一起） 能够促进黑洞双星事件的完整参数估计（PE）。然而，尽管与数值相对论波形相比，它们具有很高的效率和可靠性，但原始的 SEOBNR 代码仍然太慢，无法直接用于基于标准马尔可夫链蒙特卡罗 (MCMC) 的 PE。为了解决这个问题，PI 团队之前开发了 SEOBNR 近似值的优化版本，这使得使用 SEOBNRv3_opt 以更快的速度对候选事件执行 PE 成为可能。此外，SEOBNRv3_opt 的开发揭示了底层 SEOBNRv3 近似值中偶尔出现的病理行为。虽然这种情况只发生在大约 10,000 到 100,000 例中，但该频率仍然是 PE 的主要障碍，因为 PE 需要生成 10^8 波形实现。因此，迫切需要开发一种比 SEOBNRv3_opt 更高效、更稳健的近似方法。该项目将创建一种新的、最先进的近似值，它比现有的任何近似值都更准确、更稳健。首先，这需要创建一个基于 SEOBNRv3_opt 的新近似，但用 PI 开发的向后一体 (BOB) 合并振铃模型替换振铃附件，该模型在 NR 探测的整个参数空间范围内产生与 NR 结果一样准确的合并振铃波形。由于 BOB 可以扩展到比光环更早的时间，因此可以避免 SEOBNRv3 对精确光环位置的极度敏感，从而显着提高模型的鲁棒性。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。