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

美国国家科学基金会的LIGO探测器首次直接探测到合并黑洞发出的引力波，这是引力物理学的历史性突破，并获得了2017年的诺贝尔物理学奖。同样令人震惊的是引力波和电磁波谱信号的探测，从伽玛射线到两颗中子星的吸入和合并产生的无线电波。有了这些以及随后许多类似的探测，我们打开了一扇通往宇宙的新窗口。对中子星合并的观察提供了大量的数据，使得研究强引力和极端密度的物质成为可能。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。这个项目旨在提供公共计算机代码来模拟这样的系统。这些代码将成为推动科学进步的计算基础设施的一部分。通过与观测结果的比较，这些代码的预测可以用来推断恒星的信息，包括超核密度的状态方程。这种公开代码将使全球的数值相对论界受益，并可能促进新的合作。这项研究还将导致模拟结果（例如引力波波形）的发表，这将对新兴的引力波和多信使天文学领域有用。这项研究的一部分将与伊利诺伊大学加州分校的罗兰·哈斯及其团队密切合作进行。教师，博士后或学生参观计划。该交换项目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员，一名博士后和两名研究生组成。通过定期会议和研讨会，该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，帮助建立一支具有全球竞争力的STEM劳动力队伍。中子星合并观测中收集的大量数据只能通过与理论预测进行比较来充分分析。对于后期的激励和合并阶段，这需要大规模的数值相对论模拟。为了开始这样的模拟，需要真实的初始数据来准确地编码两颗恒星在合并之前的轨道状态。该项目的计划是加强和公开由FAU数值相对论组开发的SGRID代码。该代码可用于构建双中子星初始数据，其中恒星可以具有任意质量、自旋和轨道偏心率。此外，将为公共爱因斯坦工具包提供初始数据阅读器，以便任何人都可以使用这些初始数据来使用爱因斯坦工具包进行一般中子星双星的模拟。此外，将提供Python脚本来微调恒星的轨道参数，以允许低偏心轨道，如果两颗恒星在合并前已经相互旋转了很长时间。所有代码都将附有充足的文档，以便任何感兴趣的数值相对论组都可以使用这些代码。通过这种方式，这些公共代码将成为计算基础设施的一部分，从而使数字相对论的科学成为可能。这种基础设施将提高全球数值相对论界的能力和产出。该奖项反映了美国国家科学基金会的法定使命，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.