RUI: Numerical Simulations of Neutron Stars, Black Holes and Gravitational Radiation

该奖项支持鲍登学院（Bowdoin College）在计算物理方面的研究和教学，这是一所位于缅因州不伦瑞克的高选择性本科院校。本基金将资助数值相对论和计算天体物理学的研究，并资助本科生参与本研究。研究目标涉及中子星和黑洞的超级计算机模拟，主要是由于新一代引力波激光干涉仪（包括现在正在运行的激光干涉仪引力波天文台（LIGO））探测这些物体的引力辐射的前景。为了识别和解释任何观测到的信号，并提取它们的天体物理内容，需要对它们的来源建立理论模型。可能的来源，如双中子星和黑洞，必须在广义相对论的框架内建模。这项研究旨在发展必要的数值算法，在数值上构建这样的理论模型，并预测引力波信号，以便与未来的观测进行比较。本科生将积极参与计算物理研究小组，为他们提供“动手”的研究经验，并创造一个丰富的研究学习环境。学生将从事各种定义良好、易于管理和有意义的项目，包括，例如，对先前结果的概括和扩展，以及对简单模型问题的探索。他们还将有机会与其他机构的科学家合作，并在会议和出版物上展示他们的工作，使这种研究经历成为研究生培训和其他未来科学及其他职业的非常宝贵的准备。

This award supports a well-integrated program of research and teaching in computational physics at Bowdoin College, a highly selective undergraduate institution located in Brunswick, Maine. The awarded funds will provide support for research in numerical relativity and computational astrophysics and for undergraduate students to participate in this research. The research goals, involving supercomputer simulations of neutron stars and black holes, are primarily motivated by the prospect of detecting gravitational radiation from such objects with the new generation of gravitational wave laser interferometers, including the now operational Laser Interferometer Gravitational wave Observatory (LIGO.) To identify and interpret any observed signals, and to extract their astrophysical content, requires theoretical models of their sources. Likely sources such as binary neutron stars and black holes must be modeled within the framework of general relativity. This research is aimed at developing the necessary numerical algorithms, at numerically constructing such theoretical models, and at predicting gravitational wave signals for comparison with future observation. Undergraduate students will actively participate in a computational physics research group, providing them with a "hands-on" research experience and generating a research-enriched learning environment. The students will work on a variety of well-defined, manageable and meaningful projects, including, for example, the generalization and extension of previous results and the exploration of simple model problems. They will also have the opportunity to collaborate with scientists at other institutions and to present their work at conferences and in publications, making this research experience a very valuable preparation for graduate training and other future careers in the sciences and beyond.