论文标题
扩大裂痕：改善GW参数推理的性能
Expanding RIFT: Improving performance for GW parameter inference
论文作者
论文摘要
快速迭代拟合（RIFT）参数推理算法为GW源提供了有效，高度平行的参数推断的框架。在本文中，我们总结了Rift迭代算法的基本算法增强功能和操作点选择，包括用于分析Ligo/Pirgo O3观测值的选择。我们还描述了RIFT算法和软件生态系统的其他扩展。一些扩展可以提高裂谷的灵活性，以产生与GW天体物理学有关的产出。其他扩展提高了其计算效率或稳定性。使用许多随机选择的来源，我们使用两种不同的代码配置来评估代码鲁棒性，一种旨在模仿Ligo O3，另一种旨在使用多种性能增强功能。我们通过分析选定事件来说明Rift的功能。
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 choices 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 O3 and another employing several performance enhancements. We illustrate RIFT's capabilities with analysis of selected events.
