Flexible Analysis of Gravitational Wave Data: Extracting Information from Unmodeled or Partially Modeled Sources and Mitigating Instrument Glitches

引力波数据的灵活分析：从未建模或部分建模的源中提取信息并减轻仪器故障

• 批准号: 2110111
• 负责人: Katerina Chatziioannou
• 金额: $ 21万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110111&HistoricalAwards=false
• 关键词: Flexible; Analysis; Gravitational; Wave; Data

The field of gravitational wave astrophysics is experiencing accelerating growth since its birth in 2015. This is the outcome of large improvements in the sensitivity of gravitational wave detectors as well as the continued development of advanced tools to interpret the data the detectors collect. The wealth of signals and information brings new challenges to be addressed both in the analysis of further signals from compact binaries, such as colliding black holes and neutron stars, and in the interpretation of anticipated novel signals made accessible with more sensitive detectors. One such example concerns noise artifacts in the detectors that can occur at the same time as astrophysical signals and jeopardize our ability to interpret them. Such noise artifacts have to be understood and ideally removed from the data before any further analysis of the astrophysical signals. This project aims to address some of these emerging challenges with flexible data analysis techniques that can handle the large expected variety of detector noise artifacts and yet unseen signals. This project concerns the use of flexible, morphology-independent analyses for data analysis during the fourth observing run of LIGO as well as the development of novel analyses for the interpretation of anticipated signals such as inspiral and post merger emission from neutron star binaries. Regarding the former, past experience indicates that the increased rate of detection expected during the fourth observing run will result in more instances of astrophysical signals overlapping with instrumental glitches. This project aims to improve upon the techniques already utilized by the LIGO and Virgo Collaborations to provide more efficient glitch subtraction on data that also include an astrophysical signal of interest. Regarding the latter, this project will explore ``hybrid" data analysis techniques capable of analyzing partially modeled signals for which we lack exact waveform templates. The aim is to analyze and extract information from signals such as neutron star mergers that carry important information about the neutron star equation of state. The result of these activities will facilitate analyses of LIGO/Virgo data for extraction of astrophysical information as well as novel techniques to meet the demands of the detector improved sensitivity.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.

引力波天体物理学领域自2015年诞生以来正经历着加速发展。这是引力波探测器灵敏度大幅提高的结果，也是解释探测器收集数据的先进工具不断发展的结果。丰富的信号和信息带来了新的挑战，无论是在分析致密双星的进一步信号，如碰撞黑洞和中子星，还是在解释预期的新信号时，都需要解决更敏感的探测器。其中一个例子涉及探测器中的噪声伪影，它可能与天体物理信号同时出现，并危及我们解释它们的能力。在进一步分析天体物理信号之前，必须理解并从数据中去除这些噪声伪影。该项目旨在通过灵活的数据分析技术来解决这些新出现的挑战，这些技术可以处理各种各样的探测器噪声伪像和看不见的信号。该项目涉及在LIGO第四次观测期间使用灵活的、与形态无关的分析方法进行数据分析，以及开发新的分析方法来解释预期的信号，如中子星双星的激励和合并后发射。关于前者，过去的经验表明，在第四次观测期间预期的探测率的增加将导致更多的天体物理信号与仪器故障重叠的情况。该项目旨在改进LIGO和Virgo合作项目已经使用的技术，对包括感兴趣的天体物理信号在内的数据提供更有效的故障减法。关于后者，该项目将探索“混合”数据分析技术，能够分析我们缺乏精确波形模板的部分建模信号。目的是分析和提取中子星合并等信号中的信息，这些信号携带着中子星状态方程的重要信息。这些活动的结果将有助于对LIGO/Virgo数据的分析，以提取天体物理信息，以及满足探测器提高灵敏度要求的新技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Gravitational wave inference on a numerical-relativity simulation of a black hole merger beyond general relativity

超越广义相对论的黑洞合并数值相对论模拟的引力波推断

• DOI: 10.1103/physrevd.107.024046
• 发表时间: 2023
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Okounkova, Maria;Isi, Maximiliano;Chatziioannou, Katerina;Farr, Will M.
• 通讯作者: Farr, Will M.

Accurate modeling and mitigation of overlapping signals and glitches in gravitational-wave data

• DOI: 10.1103/physrevd.106.042006
• 发表时间: 2022-05
• 期刊: Physical Review D
• 影响因子: 5
• 作者: S. Hourihane;K. Chatziioannou;M. Wijngaarden;D. Davis;T. Littenberg;N. Cornish

Probing neutron stars with the full premerger and postmerger gravitational wave signal from binary coalescences

• DOI: 10.1103/physrevd.105.104019
• 发表时间: 2022-02
• 期刊: Physical Review D
• 影响因子: 5
• 作者: M. Wijngaarden;K. Chatziioannou;A. Bauswein;J. Clark;N. Cornish

Concurrent estimation of noise and compact-binary signal parameters in gravitational-wave data

• DOI: 10.1103/physrevd.106.104021
• 发表时间: 2022-08
• 期刊: Physical Review D
• 影响因子: 5
• 作者: C. Plunkett;S. Hourihane;K. Chatziioannou