The LIGO gravitational wave detectors are currently undergoing a major upgrade, with the goal of improving the broadband sensitivity by an order of magnitude. The scientific capabilities of the instruments can be further enhanced by improving the signal processing techniques used to tease faint gravitational wave signals out of the instrument noise. The research described in this proposal seeks to develop tools that will enhance our ability to detect and characterize transient gravitational wave signals. The initial searches for gravitational waves have taught us that non-Gaussian features in the data, such as noise transients or "glitches", impair our ability to detect weak signals. Experience with blind signal injections has taught us the importance of signal characterization and parameter estimation in assessing putative detections. Our goal is to develop and implement powerful new techniques that can help separate gravitational wave signals from instrument artifacts. This will be done in the framework of the BayesWave algorithm that was developed under a predecessor award. BayesWave provides a natural framework to develop targeted searches by incorporating prior knowledge about the signals and glitches. Multi-variate classifiers and cluster analysis tools will be used to identify glitch families based on the characterizations provided by the BayesWave analysis. These will aid in real-time detector studies and noise mitigation efforts. In the event of a detection, the signal characterization capability will help identify the type of astrophysical system that could have produced the observed waveform. These studies will become possible in near real-time using a new technique that speeds up the computation of the model likelihood by orders of magnitude. The detection of gravitational waves will allow a host of science questions to be addressed. Foremost among these will be whether the signals detected conform to the predictions of Einstein's theory of general relativity. The final component of this proposal deals with developing robust and generic techniques for identifying departures from general relativity using LIGO/Virgo data.
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 research program outlined in this proposal 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: recent graduates from the Montana State gravitational wave astronomy group have found employment as national security intelligence analysts and in the medical research field of bioinformatics. Our 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. For this proposal we plan to produce new web-based educational resources that illustrate the signal processing and classification techniques used in our research.
