Boosting Algorithmic Efficiency: Numerical Relativity in Dynamical, Curvilinear Coordinates

This award supports research in relativity and relativistic astrophysics and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Einstein’s theory of general relativity (GR) provides science's current best understanding of gravity. It predicts the existence of bizarre objects like black holes and neutron stars, and ripples in spacetime called gravitational waves.

These predictions motivated the construction of NSF's Laser Interferometer Gravitational-wave Observatory (LIGO), which has detected several gravitational wave signals from colliding black holes and neutron stars over the past years. For their efforts in making these detections possible, the leaders of LIGO were awarded the 2017 Nobel Prize in Physics.

Much of gravitational wave (GW) science depends on GW observations being compared with millions of theoretical predictions, which must be built upon GW catalogs extracted from numerical relativity (NR) simulations. NR simulations solve the GR equations in full on the computer, and to date each of these NR simulations has required a small computing cluster, which has limited throughput to only about 3,000 GWs in 15-years.

Given the vast number of possible scenarios for even the simplest and most commonly observed GW source, binary black holes (BBHs), such a small GW collection threatens potential science gains from future GW observations. BlackHoles@Home is a proposed citizen-science project leveraging new techniques to fit NR BBH simulations on a consumer-grade desktop computer, enabling new GW catalog generation with unprecedented throughput using volunteer computers.

Such throughput will enable far more detailed analyses of observed GWs from current and future GW detectors, maximizing the science gained from hard-fought observations. To educate the public and advertise this volunteer computing project both locally and globally, convocations will be given in underserved high schools, and updates will be posted to a widely disseminated BlackHoles@Home email newsletter.

Improvements to the algorithmic and mathematical underpinnings of NR codes have recently culminated in a coming-of-age for the field, moving it beyond proof-of-principle calculations and into the realm of predictive astrophysics. Over the past six years, NR-based theoretical predictions of gravitational waves (GWs) were central to uncovering the binary parameters in LIGO and Virgo's recent GW discoveries.

Looking ahead, GW catalogs generated by NR simulations of compact binaries will need to grow greatly to ensure that parameter estimation accuracy can keep up with increased sensitivity of GW interferometers. BlackHoles@Home is a proposed BOINC project that aims to fit binary black hole (BBH) simulations on the consumer-grade desktop computer. In doing so the general public can be enlisted to help generate the large GW catalogs that form the foundation for a great deal of GW science.

Traditionally, these BBH simulations have been performed on supercomputers. BlackHoles@Home implements new approaches for robustly solving Einstein's equations of general relativity in highly efficient coordinate systems, so that these simulations will fit on consumer-grade desktop computers in only a few gigabytes of RAM. BlackHoles@Home's core infrastructure provides a firm foundation for compact binary simulations beyond BBHs.

To this end, the dynamical-spacetime GRMHD code IllinoisGRMHD will be incorporated into this infrastructure to enable state-of-the-art binary neutron star simulations on supercomputers. These simulations will leverage both recent advances in solving the GRMHD equations in spherical-like coordinate systems, as well as recent improvements to IllinoisGRMHD that add both advanced nuclear equation of state support and basic neutrino physics.

Monte-Carlo-based photon and neutrino feedback will also be incorporated to enable state-of-the-art realism in these binary neutron star simulations.

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.