Searches for Transient Gravitational Wave Signals

该奖项支持旨在扩展激光干涉仪引力波天文台（LIGO）检测来自各种瞬态源（包括黑洞合并和核心坍缩超新星）的引力波信号的能力的研究。对瞬态引力波信号的增强搜索将采用强大的约束信号重建技术，使用来自引力波探测器网络的数据。这种方法的一个关键要素是描述信号的先验知识的约束的发展。黑洞合并和核心坍缩超新星的数值模拟的信息将被用来构建源特定的先验输入。一旦进行了检测，重要的是信号是为描述信号的参数建立的特征值。这将通过马尔可夫链蒙特卡罗采样例程的实施来研究旋转黑洞inspiral信号和未建模的爆发信号。这些实现将被集成到标准的LIGO搜索管道中，使用现有的基础设施来计算信号的似然性。这些研究将与LIGO科学合作数据分析工作组密切合作进行。LIGO希望探测到的大多数引力波信号都不会被注意到，隐藏在仪器噪音中，没有复杂的数据分析技术。数据分析的增强与硬件升级一样有价值。LIGO项目为年轻的研究人员和学生提供了参与新观测科学诞生的机会，这一科学将带来天文学的革命性发现，并为宇宙中一些最奇特的现象提供独特的见解。该奖项支持的研究项目为研究生和本科生提供了学习这些数据分析技术的机会，这些技术具有更广泛的适用性。此外，一个关于引力波天文学的讲座计划正在制定中，将被带到蒙大拿州各地的学校。

This award supports research designed to extend the ability of the Laser Interferometer Gravitational-wave Observatory (LIGO) to detect gravitational wave signals from a variety of transient sources, including black hole mergers and core collapse supernovae. The enhanced searches for transient gravitational wave signals will employ the powerful technique of constrained signal reconstruction using data from a network of gravitational wave detectors. A key element of this approach is the development of constraints that describe prior knowledge of the signals. Information from numerical simulations of black hole mergers and core collapse supernovae will be used to construct source specific prior input. Once a detection has been made it is important that the signal be characterized values established for the parameters that describe the signal. This will be done through implementation of Markov Chain Monte Carlo sampling routines to study spinning black hole inspiral signals and un-modeled burst signals. These implementations will be integrated into the standard LIGO search pipelines using the existing infrastructure to compute signal likelihoods. These studies will be carried out in close collaboration with the LIGO Scientific Collaboration data analysis working groups. The majority of the gravitational wave signals that LIGO hopes to detect would pass by unnoticed, hidden in the instrument noise, without sophisticated data analysis techniques. Enhancements in data analysis can be just as valuable as hardware upgrades. The LIGO project presents young researchers and students with the 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 research program supported by this award offers opportunities for graduate and undergraduate students to learn these data analysis techniques with potentially broader applicability. In addition, a lecture program on gravitational wave astronomy is being developed that will be taken to schools throughout Montana.