A gravitational wave astrophysics driven by observations is just around the corner. Interferometric gravitational-wave detectors such as LIGO and its partners have reached design sensitivity. The general consensus is that first detections will take place in the very near future. Beyond detection, one of the grand challenges in this new astronomy is the characterization of sources from the information encoded in the signals buried in the noisy data. The degree of success in this enterprise will determine the extent to which gravitational wave observations can be used as a tool of discovery. The proposed project is motivated by the premise that recognizing and interpreting the data collected through the window of gravitational wave observations requires a set of skills spanning several disciplines, thus presenting an opportunity to develop transformative and multidisciplinary research and to open the door to innovations and advances in this new astronomy, in which gravitational waves act as messengers.
The central theme of this project is using numerical-relativity tools and data-analysis methodologies to solve the inverse problem in gravitational physics for one of the most important sources of gravitational radiation, the inspiral and merger of a binary black hole system. The solution to this problem is essential for harnessing the predictive power of general relativity and for enhancing the conversation between the data collected by gravitational wave interferometric detectors and the questions posed by astrophysics.
The project approach is development of efficient numerical algorithms for assimilating observational data, applying deterministic and stochastic parameter estimation techniques to address the source characterization problem. Activities supported by this proposal are intended to produce tools and methodologies that enable the marriage of numerical simulation, gravitational wave observation and algorithms used in large inverse problems outside gravitational physics. These tools will enable acquiring new knowledge through the analysis of large and rich multi-spectral gravitational observations, multi-scale numerical relativity simulations, catalyzed by the combination of the still-growing pool of computational resources and related advances in the understanding of computational Bayesian inference.
Among its broader impacts, this multidisciplinary team intends to develop a new paradigm for using numerical relativity tools and an innovative approach to the inverse problem in gravitational physics. The tools produced by this collaboration will be made freely available and will be directly applicable to other disciplines. The proposed work exploits algorithms and methodology to tackle inverse problems in areas such as global climate change, weather forecasting, medical imaging and reservoir simulations. Students will have opportunities to build expertise in data analysis, numerical algorithms, high-performance computing, optimization, and inverse problems.
