Advanced LIGO Search for the Stochastic Gravitational Wave Background

随机引力波背景的高级 LIGO 搜索

基本信息

批准号：
1806630
负责人：
Vuk Mandic
金额：
$ 39.6万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1806630&HistoricalAwards=false
关键词：
Advanced LIGO Search Stochastic Gravitational

项目摘要

Recent observation by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and by Advanced Virgo (aVirgo) detectors of mergers of binary black hole and binary neutron star systems have opened the field of multi-messenger astrophysics. They have sparked a very broad range of studies, from new tests of General Relativity and the measurement of the Hubble constant to constraints on the equation of state in neutron stars and studies of the r-process of heavy element production in the binary neutron star merger. Adding the gravitational wave signals from all such binaries in the universe leads to a stochastic gravitational wave background (SGWB). The above discoveries have enabled a robust estimate of this background, indicating that it is within reach of the upcoming observation runs of aLIGO and aVirgo. The SGWB may well be the next new type of gravitational-wave signal to be discovered by these detectors, with the discovery coming potentially as early as 2019. This project aims to measure (and detect) the SGWB using data from upcoming observation runs of aLIGO and aVirgo, improving the sensitivity by up to 100x relative to the most recent results. The project will support involvement of graduate and undergraduate students in research at the frontier of the nascent field of multi-messenger astrophysics, as well as activities designed to share the excitement of this new field with broader audience.More specifically, using cross-correlation techniques applied to aLIGO and aVirgo data, the frequency content and temporal structure of the SGWB will be measured. Bayesian parameter estimation framework will then use this information to estimate the contributions of various astrophysical and cosmological SGWB models, as well as to identify and remove environmental contamination, such as due to the Schumann magnetic resonances. The results of this analysis are expected to place stringent constraints on the formation and evolution of compact binary systems, hence illuminating the evolution of the observed structure in the universe. Furthermore, these results will have the potential to constrain cosmological SGWB models, such as inflationary or cosmic (super)string models, and therefore probe the physics of fundamental interactions at very high energies, unachievable in laboratories. Similar cross correlation techniques will also be used to develop new searches for long transient signals, lasting from hours to days or weeks, which are expected in multiple models of neutron stars and are of particular interest for studying the remnants of binary neutron star mergers. If detected, such a signal would provide information about the physics processes driving Gamma Ray Bursts and about the high-density state of nuclear matter in neutron stars. To further improve the sensitivity of these searches, deep machine learning techniques will be used to remove the environmental contamination from the aLIGO and aVirgo gravitational-wave data.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.

先进激光干涉引力波天文台（aLIGO）和先进处女座（aVirgo）探测器最近对双黑洞和双中子星系统合并的观测开启了多信使天体物理学领域。它们引发了非常广泛的研究，从广义相对论的新测试和哈勃常数的测量到中子星状态方程的约束以及双中子星合并中重元素产生的 r 过程的研究。将宇宙中所有此类双星的引力波信号相加就得到了随机引力波背景（SGWB）。上述发现能够对这一背景进行可靠的估计，表明 aLIGO 和 aVirgo 即将进行的观测运行可以达到这一背景。 SGWB 很可能是这些探测器发现的下一个新型引力波信号，最早可能在 2019 年发现。该项目旨在使用 aLIGO 即将进行的观测运行的数据来测量（和检测）SGWB和 aVirgo，相对于最新结果，灵敏度提高了 100 倍。该项目将支持研究生和本科生参与多信使天体物理学新兴领域的前沿研究，以及旨在与更广泛的受众分享这一新领域的兴奋的活动。更具体地说，使用互相关技术应用到 aLIGO 和 aVirgo 数据，将测量 SGWB 的频率内容和时间结构。然后，贝叶斯参数估计框架将使用这些信息来估计各种天体物理和宇宙学 SGWB 模型的贡献，以及识别和消除环境污染，例如由于舒曼磁共振造成的环境污染。这项分析的结果预计将对致密双星系统的形成和演化施加严格的约束，从而阐明宇宙中观测到的结构的演化。此外，这些结果将有可能限制宇宙学 SGWB 模型，例如暴胀或宇宙（超）弦模型，从而探索实验室无法实现的极高能量下基本相互作用的物理现象。类似的互相关技术也将用于开发对持续数小时到数天或数周的长瞬态信号的新搜索，这在中子星的多个模型中是预期的，并且对于研究双中子星合并的残余物特别感兴趣。如果检测到，这样的信号将提供有关驱动伽马射线爆发的物理过程以及中子星中核物质高密度状态的信息。为了进一步提高这些搜索的灵敏度，将使用深度机器学习技术来消除 aLIGO 和 aVirgo 引力波数据中的环境污染。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Noise reduction in gravitational-wave data via deep learning

通过深度学习降低引力波数据的噪声

DOI：
10.1103/physrevresearch.2.033066
发表时间：
2020
期刊：
Physical Review Research
影响因子：
4.2
作者：
Ormiston, Rich;Nguyen, Tri;Coughlin, Michael;Adhikari, Rana X.;Katsavounidis, Erik
通讯作者：
Katsavounidis, Erik

Measuring angular N -point correlations of binary black hole merger gravitational-wave events with hierarchical Bayesian inference