This award supports the development of software infrastructure for large scale simulations of gravitational wave sources (numerical relativity) through a collaboration among researchers at Louisiana State University, Pennsylvania State University, and Rochester Institute of Technology. Recent breakthroughs have provided the numerical relativity community with the basic techniques in to evolve binary black holes for multiple orbits, including the merger and final black hole ring-down phases. Information from these computer simulations have the potential both to enhance the likelihood that ground-based and space-based gravitational wave detectors will recognize these gravitational wave signals from these exotic events and to improve the ability to determine the properties of these systems if signals are detected. The infrastructure to be developed, called XiRel, will build upon and integrate several well developed and widely used computational infrastructures and emerging standards, including Cactus and Carpet. The initial and primary focus of this project is the development of a highly scalable, efficient and accurate adaptive mesh refinement layer based on the existing Carpet driver, which will be fully integrated and supported in Cactus and optimized for numerical relativity. This project will be driven by the scientific goal to perform accurate simulations of black hole binaries with larger initial separations than currently possible, with un-equal masses and unequal spins, and providing reliable physical results critical for gravitational wave astronomy and astrophysics. This award is supported by the Division of Physics in the Mathematical and Physical Sciences Directorate and by the Division of Computing and Communication Foundations in the Computer and Information Science and Engineering Directorate.
