Detection and Characterization of Gravitational Wave Transients

2015年9月14日，先进的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从仪器噪声中梳理微弱引力波信号的算法来提高瞬态引力波信号搜索灵敏度的研究，并提供有关信号物理特征的详细信息，以便我们能够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BayesWave算法的基础上，该算法是在两次之前的奖励下开发的。BayesWave将引力波爆发信号与仪器噪音的噼啪声分离开来。BayesWave分析帮助确认了引力波的首次探测，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们提供了一个绝佳的机会，让他们参与到一门新的观测科学的诞生中来，这门科学有望做出一些发现，这些发现将彻底改变天文学，并为宇宙中一些最奇特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大量的机会。与开发复杂和创新的数据分析技术相关的创造性活动，结合实际操作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出色的培训。这些技能是可转移的，在其他领域也很受欢迎。密歇根州立大学小组一直非常积极地通过讲座、学校讲座项目和纪录片的制作，将引力波科学带给公众。该小组计划制作新的网络教育资源，通过将其应用于听觉信号分析的相关问题来说明他们研究中使用的信号处理技术。所支持的工作将在几个方面改进和扩展BayesWave算法，包括开发针对特定信号的目标搜索，提供有关信号的物理信息提取的新功能，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，高离心率系统的爆发序列，以及大质量黑洞双星的启发，合并和环灭。在这些有针对性的分析中使用的物理参数化模型将使我们能够对致密物体的质量、自旋和半径等数量进行估计。对于来自未知来源的信号，重要的是要彻底表征信号，以便与可能的天体物理模型联系起来。为此，该小组将开发新的工具来提取信号的时频发展信息，以及相关的测量，如上升和衰减时间。该算法的低延迟版本将提供一种新的前线搜索功能，以补充现有的突发搜索管道，并提供冗余。

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.