RAISE: Creation of an Advanced Public Binary Neutron Star Initial Data Code for the Einstein Toolkit

NSF的LIGO探测器首次直接探测到黑洞合并发出的引力波，这是引力物理学的历史性突破，并于2017年获得诺贝尔物理学奖。同样令人震惊的是，从两颗中子星的螺旋和合并中探测到了引力波和电磁波谱信号，从伽马射线到无线电波。随着这些以及随后无数类似的探测，我们打开了一扇通往宇宙的新窗口。对中子星星合并的观测提供了大量的数据，使得研究强引力和极端密度下的物质成为可能。当两颗恒星靠近时，需要对爱因斯坦方程进行完全非线性的数值模拟来解释观测数据。该项目旨在提供公共计算机代码来模拟这种系统。这些代码将成为计算基础设施的一部分，使科学进步成为可能。通过与观测结果进行比较，这些代码的预测可以用来推断有关恒星的信息，包括超核密度的状态方程。这种公共代码将有利于全球数值相对论社区，并可能促进新的合作。这项研究还将导致模拟结果（例如引力波形）的出版，这将有助于引力波和多信使天文学的新兴领域。这项研究的一部分将与罗兰哈斯和他在UIUC的小组密切合作进行。计划由教师，博士后或学生访问。这项交流将为佛罗里达大西洋大学（FAU）的学生和博士后提供教育福利。此外，该奖项将支持FAU的数值相对论小组，该小组目前由一名教师，一名博士后和两名研究生组成。通过定期的会议和研讨会，这个小组帮助培养学生和博士后在广泛的主题，从广义相对论和天体物理学到计算机科学和大规模计算。这将为他们提供宝贵的技能，这将有助于建立一个具有全球竞争力的STEM劳动力。在中子星星合并的观测中收集的大量数据只能通过将它们与理论预测进行比较来进行充分分析。对于后期的螺旋和合并阶段，这需要大规模的数值相对论模拟。为了开始这种模拟，需要真实的初始数据，准确地编码合并前轨道上两颗恒星的状态。该项目计划是增强和公开由FAU数值相对论小组开发的SGRID代码。该程序可用于构造双星中子星星的初始数据，其中恒星可以具有任意的质量、自旋和轨道偏心率。此外，还将提供一个用于公共爱因斯坦工具包的初始数据读取器，以便任何人都可以使用这些初始数据来利用爱因斯坦工具包模拟一般的中子星星双星。此外，Python脚本将提供微调恒星的轨道参数，以允许低偏心率轨道，如果两颗恒星在合并之前已经相互盘旋了很长一段时间。所有的代码将伴随着丰富的文档，以便任何感兴趣的数值相对论组可以使用这些代码。通过这种方式，这些公共代码将成为使数值相对论科学成为可能的计算基础设施的一部分。这种基础设施将提高全球数值相对论社区的能力和产出。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The first direct detection of gravitational waves emitted from merging black holes by the NSF's LIGO detectors was a historic breakthrough in gravitational physics and led to the Noble prize in physics in 2017. Equally stunning was the detection of both gravitational waves and signals across the electromagnetic spectrum from gamma rays to radio waves from the inspiral and merger of two neutron stars. With these and numerous subsequent similar detections a new window to the universe has been opened. Observations of neutron star mergers provide a vast amount of data that allows the study of both strong gravity and matter at extreme densities. As the two stars get close, fully non-linear numerical simulations of the Einstein equations are required to interpret the observational data. This project is aimed at providing public computer codes to simulate such systems. These codes will become part of the computational infrastructure that enables progress in science. By comparing to observations, predictions from these codes can be used to infer information about the stars, including the equation of state at supra nuclear density. Public codes of this kind will benefit the global numerical relativity community, and may foster new collaborations. This research will also lead to the publication of simulation results (e.g. gravitational waveforms) which will be useful for the emerging field of gravitational wave and multimessenger astronomy. Part of this research will be carried out in close collaboration with Roland Haas and his group at UIUC. Visits by faculty, postdocs or students are planned. This exchange will have educational benefits for students and postdocs at Florida Atlantic University (FAU). In addition, the award will support the numerical relativity group at FAU, which currently consists of one faculty member, one postdoc, and two graduate students. Through regular meetings and seminars this group helps to train students and postdocs in a wide range of topics ranging from general relativity and astrophysics to computer science and large scale computing. This will give them valuable skills that will help build a globally competitive STEM workforce.The large amounts data collected in the observations of neutron star mergers can only be fully analyzed by comparing them to theoretical predictions. For the late inspiral and merger phases this requires large scale numerical relativity simulations. In order to even start such simulations realistic initial data are needed that accurately encode the state of both stars in orbit prior to merger. The project plan is to enhance and make public the SGRID code that has been developed by the numerical relativity group at FAU. This code can be used to construct binary neutron star initial data where the stars can have arbitrary masses and spins and orbital eccentricities. In addition, an initial data reader for the public Einstein Toolkit will be provided, so that these initial data can be used by anyone to perform simulations of generic neutron star binaries with the Einstein Toolkit. Further, Python scripts will be provided to fine tune the stars' orbital parameters to allow for low eccentricity orbits that are expected if the two stars have been spiraling toward each other for a long time before merger. All codes will be accompanied by ample documentation so that any interested numerical relativity group can use these codes. In this way these public codes will become part of the computational infrastructure that enables science in numerical relativity. This kind of infrastructure will enhance the capabilities and the output of the global numerical relativity community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.