Gravitational Wave Data Analysis: Parameter Inference and Black Hole Ringdown

引力波数据分析：参数推断和黑洞衰荡

• 批准号: 2308833
• 负责人: Aaron Zimmerman
• 金额: $ 24万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308833&HistoricalAwards=false
• 关键词: Gravitational; Wave; Data; Analysis; Parameter

This research project is devoted to the analysis and modeling of gravitational waves. These ripples in the fabric of space and time are produced when the densest objects in the Universe -- such as black holes and neutron stars -- orbit and collide. The detection of gravitational waves by the NSF-funded LIGO and European Virgo collaborations has opened up a new window on the Universe, revealing previously invisible events like pairs of black holes merging. Researchers supported by this award will participate in the analysis of gravitational waves as members of the LIGO collaboration, helping to infer the properties of the sources of these ripples. In addition to these collaboration activities, which have far-reaching impacts on astrophysics, cosmology, and nuclear physics, the research group will carry out searches for new physics using gravitational-wave data. Detections of gravitational waves provide information about spacetime at its most dynamic, allowing for strong-field tests of relativity, as the possibility of detecting extremely compact objects which are otherwise invisible to electromagnetic observations. This award will enable both searches for gravitational waves from new, hypothetical compact stars, and study how the final "ringdown" of merged black holes can be used to search for new physics. These research activities will provide training for graduate students in modeling and data analysis, skillsets which are in great demand and of great benefit to society. In addition, members of the group will engage in outreach activities in order to educate and inspire the public about this new field of physics and astronomy.In particular, this project focuses supporting the LIGO Scientific Collaboration by performing Bayesian parameter estimation on detections made in the collaboration's next observing campaign. The PI and other members of the group will participate in the application of parameter estimation pipelines, carrying out inferences on detections made in low latency and helping to plan and execute comprehensive parameter analysis on longer timescales. These inferences are a crucial first step in the use of gravitational waves for understanding the populations of compact objects, inferring the nuclear equation of state from observations of binary neutron stars, testing the theory of relativity, and using gravitational waves to measure cosmic expansion. This project will also involve the investigation of systematic errors in parameter estimation when using cutting edge models, which exhibit complicated parameter degeneracies. The team will also use methods borrowed from quantum mechanical perturbation theory to predict how possible deviations from relativity are imprinted on the ringdown spectrum of spinning black holes. These predictions can be used in the future with Bayesian parameter estimation to constrain or measure the underlying parameters of these models using ringdown signals from binary black hole mergers.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.

该研究项目致力于重力波的分析和建模。当宇宙中最密集的物体（例如黑洞和中子星）的轨道和碰撞时，会产生这些空间和时间结构的涟漪。 NSF资助的Ligo和欧洲处女座合作对引力波的检测为宇宙打开了一个新窗口，揭示了以前看不见的事件，例如合并的黑洞对。该奖项支持的研究人员将参与引力波的分析，作为Ligo合作的成员，有助于推断这些涟漪来源的特性。除了这些合作活动对天体物理学，宇宙学和核物理学有深远影响的协作活动外，研究小组还将使用重力波数据进行搜索新物理学。重力波的检测提供了有关时空最动态的信息，从而可以进行强场测试，因为检测到极其紧凑的物体的可能性是电磁观察值。该奖项将使新的，假设的紧凑型恒星都可以搜索引力波，并研究合并后的黑洞的最终“响声”如何用于寻找新的物理学。这些研究活动将为研究生进行建模和数据分析，对社会有很大需求和巨大好处的技能提供培训。此外，该小组的成员将参加外展活动，以教育和激发公众对这个新的物理和天文学领域的启发。尤其是，该项目着重于支持Ligo Scientific Collaboration，通过对贝叶斯的参数估算进行协作中的检测，以支持Ligo Scientific合作。 PI和该组的其他成员将参与参数估计管道的应用，对低潜伏期中的检测进行推断，并帮助计划和执行对较长时间标准的全面参数分析。这些推论是使用引力波来理解紧凑物体种群的关键第一步，从二进制中子恒星的观察，测试相对性理论并使用引力波来测量宇宙扩张。当使用尖端模型时，该项目还将涉及参数估计中的系统误差的研究，这些模型表现出复杂的参数退化。该团队还将使用从量子机械扰动理论中借用的方法来预测在旋转黑洞的环down频谱上的可能偏差。这些预测可以在未来使用贝叶斯参数估计来使用，以使用二进制黑洞合并中的RINGDOWN信号来限制或测量这些模型的基本参数。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。