Searching for the Stochastic Gravitational Wave Background with Advanced Gravitational Wave Detectors

该奖项支持引力波数据分析的研究，并解决了NSF“宇宙之窗”大构想的优先领域。随机引力波背景是由宇宙中不相关的天体物理和宇宙学源产生的许多引力波信号的非相干叠加而产生的。测量这一背景的天体物理成分，例如由于黑洞和/或中子星的合并，将提供有关宇宙结构如何形成的信息。同样，测量这个背景的宇宙学成分将提供关于宇宙年龄不到一秒时的独特信息，以及适用于非常高的能量尺度的物理定律，这些定律在实验室中是无法重现的。该项目旨在利用即将到来的高级LIGO、Virgo和Kagra数据搜索和探测随机引力波背景，并表征其组成特征，研究不同天体物理和宇宙学模型的含义。该项目将使本科生和研究生参与前沿研究，并将向公众推广引力波科学。随机引力波背景（SGWB）是由宇宙中不相关的天体物理或宇宙源产生的许多引力波信号的非相干叠加而产生的。该项目旨在利用即将在2022/23年进行的第四次观测运行（O4）的aLIGO， aVirgo和KAGRA数据搜索（并检测）SGWB，利用预期O4的改进探测器灵敏度并部署两种搜索技术。传统的基于互相关的搜索预计将比基于前三次观测的最新结果灵敏度高10倍，预计在2024年及以后，A+探测器的灵敏度将再提高4倍。对于双黑洞合并引起的SGWB的具体情况，将开发全贝叶斯搜索，相对于相互关联搜索，其灵敏度可能提高约1000倍。这些搜索将探测紧凑双星系统的高红移分布，并提供有关它们形成和演化的信息。它们还将约束宇宙学的SGWB模型，如暴胀、相变和宇宙（超）弦模型，从而探索在非常高的能量下基本相互作用的物理学，这在实验室是无法实现的。相互关联和贝叶斯SGWB搜索都将估计SGWB的方向性内容，使未来的研究能够将SGWB与宇宙中物质结构的电磁示踪剂（如星系计数或引力透镜）相关联。除了让研究生和本科生参与前沿研究外，该项目还包括一系列旨在将引力波科学的兴奋带给双子城和明尼苏达州广泛社区的活动，包括在K-12学校的物理演示和演讲，以及在K-12教室实施的数据分析项目。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。

This award supports research in gravitational waves data analysis and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Stochastic gravitational-wave background arises as an incoherent superposition of many gravitational wave signals generated by uncorrelated astrophysical and cosmological sources throughout the universe. Measuring the astrophysical component of this background, for example due to mergers of black holes and/or neutron stars, would provide information about how the structure in the universe formed. Similarly, measuring the cosmological component of this background would provide unique information about the universe when it was a fraction of a second old, and about the physical laws that apply to very high energy scales that are not reproducible in laboratories. This project aims to search and detect the stochastic gravitational wave background using the upcoming Advanced LIGO, Virgo, and Kagra data, as well as to characterize its composition and study implications for different astrophysical and cosmological models. The project will enable involvement of undergraduate and graduate students in frontier research, and it will promote gravitational-wave science to the general public.Stochastic gravitational-wave background (SGWB) arises as an incoherent superposition of many gravitational wave signals generated by uncorrelated astrophysical or cosmological sources throughout the universe. This project aims to search for (and detect) the SGWB using the upcoming aLIGO, aVirgo, and KAGRA data from the fourth observing run (O4) in 2022/23, leveraging the improved detector sensitivities expected for O4 and deploying two search techniques. The traditional cross-correlation based search is expected to be 10 times more sensitive than the most recent results based on the first three observation runs, with another factor of 4 improvement expected with the A+ detector sensitivities in 2024 and beyond. For the specific case of the SGWB due to binary black hole mergers, the full Bayesian Search will be developed with the potential to improve the sensitivity by ~1000 times relative to the cross-correlation search. These searches will probe the high-redshift distribution of the compact binary systems and provide information about their formation and evolution. They will also constrain cosmological SGWB models, such as inflationary, phase transitions, and cosmic (super)string models, and therefore probe the physics of fundamental interactions at very high energies, unachievable in laboratories. Both the cross-correlation and the Bayesian SGWB searches will estimate the directional content of the SGWB, enabling future studies correlating the SGWB with electromagnetic tracers of matter structure in the universe, such as galaxy counts or gravitational lensing. In addition to involving graduate and undergraduate students in frontier research, this project includes a series of activities designed to bring the excitement of the gravitational-wave science to broad communities in the Twin Cities and Minnesota, including physics demonstrations and presentations in K-12 schools and data analysis projects for implementation in K-12 classrooms.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.