Improving performance for gravitational-wave parameter inference with an efficient and highly-parallelized algorithm
Improving performance for gravitational-wave parameter inference with an efficient and highly-parallelized algorithm
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DOI：
10.1103/physrevd.107.024040
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发表时间：
2023-01
影响因子：
5
通讯作者：
J. Wofford;A. Yelikar;Hannah Gallagher;E. Champion;D. Wysocki;V. Delfavero;J. Lange;C. Rose;V. Valsan;S. Morisaki;J. Read;C. Henshaw;R. O’Shaughnessy
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文献类型：
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作者：
J. Wofford;A. Yelikar;Hannah Gallagher;E. Champion;D. Wysocki;V. Delfavero;J. Lange;C. Rose;V. Valsan;S. Morisaki;J. Read;C. Henshaw;R. O’Shaughnessy
快速迭代拟合(RIFT)参数推理算法为GW源的高效、高度并行化的参数推理提供了一个框架。在本文中，我们总结了裂隙迭代算法的基本算法增强和工作点选择，包括用于分析LIGO/Virgo 03观测的设置。我们还描述了对RIFT算法和软件生态系统的其他扩展。一些扩展增加了RIFT的灵活性，以产生与GW天体物理学相关的输出。其他扩展提高了它的计算效率或稳定性。使用许多随机选择的来源，我们使用两种不同的代码配置评估代码的健壮性，一种旨在模拟LIGO/Virgo 03的设置，另一种采用几种性能增强。我们通过对选定事件的分析来说明RIFT的能力。
The rapid iterative fitting (RIFT) parameter inference algorithm provides a framework for efficient, highly parallelized parameter inference for GW sources. In this paper, we summarize essential algorithm enhancements and operating point choices for the RIFT iterative algorithm, including settings used for analysis of LIGO/Virgo O3 observations. We also describe other extensions to the RIFT algorithm and software ecosystem. Some extensions increase RIFT’s flexibility to produce outputs pertinent to GW astrophysics. Other extensions increase its computational efficiency or stability. Using many randomly selected sources, we assess code robustness with two distinct code configurations, one designed to mimic settings as of LIGO/Virgo O3 and another employing several performance enhancements. We illustrate RIFT’s capabilities with analysis of selected events.