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

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.