Detection and Characterization of Gravitational Wave Transients

引力波瞬变的检测和表征

• 批准号: 1607343
• 负责人: Neil Cornish
• 金额: $ 30万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607343&HistoricalAwards=false
• 关键词: Detection; Characterization; Gravitational; Wave; Transients

The detection by Advanced LIGO of a gravitational wave signal on September 14, 2015 heralds the beginning of a new branch of astronomy. This award supports research to enhance the sensitivity of searches for transient gravitational wave signals by improving the algorithms used to tease faint gravitational wave signals out of the instrument noise, and to provide detailed information about the physical characteristics of the signals so that we can connect them to possible astrophysical sources. The proposed research will build upon the BayesWave algorithm that was developed under two predecessor awards. BayesWave separates gravitational wave burst signals from the pops and crackles of the instrument noise. The BayesWave analysis helped confirm the first detection of gravitational waves, and results from the analysis can be found in the first figure of the discovery paper. The LIGO project presents young researchers and students with a wonderful opportunity to participate in the birth of a new observation science that is poised to make discoveries that will revolutionize astronomy and deliver unique insights into some of the Universe's most exotic phenomena. The MSU research program offers tremendous opportunities for graduate and undergraduate students. The blend of creative activities associated with the development of sophisticated and innovative data analysis techniques, combined with hands on exposure to running existing search pipelines and working with production level computer code, will provide excellent training for the next generation of gravitational wave astronomers. These skills are transferable and highly sought after in other fields. The MSU group has been very active in bringing gravitational wave science to the public through talks, a school lecture program, and the production of a documentary. The group plans to produce new web-based educational resources that illustrate the signal processing techniques used in their research by applying them to related problems in auditory signal analysis.The supported work will improve and extend the BayesWave algorithm in several ways, including the development of targeted searches for specific signals, providing new functionality in the extraction of physical information about the signal that can aid in the identification of the source, and developing a low-latency capability. The new directed searches will target the post-merger signals from neutron star binaries, burst-trains from high eccentricity systems, and the late inspiral, merger and ringdown of high mass black hole binaries. The physically parameterized models used in these targeted analyses will allow us to produce estimates for quantities such as the masses, spins, and radii of the compact objects. For signals from unknown sources it is important to thoroughly characterize the signal to make connection with possible astrophysical models for the source. To this end, the group will develop new tools to extract information about the time-frequency development of the signal, and related measures such as rise and decay times. A low-latency version of the algorithm will provide a new frontline search capability that will compliment, and give redundancy to, the existing burst search pipelines.

2015年9月14日，高级LIGO探测到引力波信号，预示着天文学一个新分支的开始。该奖项支持通过改进用于从仪器噪声中梳理微弱引力波信号的算法来提高搜索瞬时引力波信号的灵敏度的研究，并提供关于信号物理特征的详细信息，以便我们可以将它们与可能的天体物理源联系起来。拟议的研究将建立在贝叶斯波算法的基础上，该算法是在两个前身奖项下开发的。BayesWave将引力波爆发信号与仪器噪声的爆裂和爆裂分开。贝叶斯波分析帮助确认了第一次探测到引力波，分析结果可以在发现论文的第一张图中找到。LIGO项目为年轻的研究人员和学生提供了一个极好的机会，参与到一门新的观测科学的诞生中来，这门科学即将做出将给天文学带来革命性变化的发现，并对宇宙中一些最奇异的现象提供独特的见解。密歇根州立大学的研究项目为研究生和本科生提供了巨大的机会。与开发复杂和创新的数据分析技术有关的创造性活动的结合，再加上亲自操作运行现有搜索管道和使用生产级计算机代码，将为下一代引力波天文学家提供极好的培训。这些技能是可以转让的，在其他领域也很受欢迎。密歇根州立大学的小组一直非常积极地通过演讲、学校讲座和制作纪录片将引力波科学带给公众。该小组计划制作新的基于网络的教育资源，通过将信号处理技术应用于听觉信号分析中的相关问题来说明他们研究中使用的信号处理技术。支持的工作将在几个方面改进和扩展BayesWave算法，包括开发对特定信号的定向搜索，提供提取有关信号的物理信息的新功能以帮助识别信号源，以及开发低延迟能力。新的定向搜索将针对中子星双星合并后的信号，来自高偏心系统的爆发序列，以及高质量黑洞双星的晚期激发、合并和环化。在这些定向分析中使用的物理参数模型将使我们能够对致密物体的质量、自转和半径等量进行估计。对于来自未知源的信号，重要的是要彻底地描述信号的特征，以便与可能的源天体物理模型相联系。为此，该小组将开发新的工具来提取关于信号的时频发展的信息，以及相关的测量方法，如上升和衰减时间。该算法的低延迟版本将提供新的前线搜索能力，该能力将补充现有的突发搜索管道，并提供冗余。