Detection and Characterization of Gravitational Wave Transients

点击翻译按钮获取中文摘要

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.