MRI: Acquisition of a High-Performance Computing Cluster to Unveil the Sources of Gravitational Waves

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This grant will allow the purchase of a high-performance-computing cluster, which is required by researchers at Northwestern University to investigate sources of gravitational-wave emission. Modeling and understanding of gravitational-wave sources is becoming extremely important as scientists are utilizing data from the Laser Interferometer Gravitational-wave Observatory to detect black holes and other exotic astrophysical objects (such as "neutron stars"). On one level, astronomers require large computers to develop programs that can help us understand the large amounts of data from gravitational-wave detectors, and pick out the real, astronomical sources from a wide variety of noise sources. But in addition, as detections of gravitational-wave sources occur more frequently, astronomers need to understand what those detections can tell us about the population of exotic objects throughout the universe. For instance, given the detections of gravitational-wave sources already, researchers ask how many black holes there might be, in our galaxy, and in other galaxies, and how massive are they? Also, researchers ask: how do those black holes form, and how do systems of pairs of black holes form? These are important questions in understanding objects at the extreme of the known laws of physics, and to answer these questions, astronomers need to understand not only the lives of individual stars and the formation of black holes and neutron stars, but they need to simulate populations of millions of stars, and the wide variety of events and processes that can affect the development of those stars. For that work, astrophysicists require large collections of very efficient computers, such as the computer cluster that will be purchased with these funds, to run massive simulations that help us explore these questions. In addition to the pure research that will be done with this cluster, the PI's group is also well known for generating award-winning visualizations of their simulations, which will be used at a variety of Science, Technology, Engineering, and Mathematics (STEM) education and public-outreach events. The entire team also has extensive experience in attracting a diverse group of researchers to STEM research and fields; this cluster will help that group to train a new generation of diverse researchers in data-science methods and scientific computing, contributing to the technical workforce of the nation. This grant will allow the acquisition of a high-performance computing (HPC) cluster that will enable research in the emerging area of gravitational physics. The cluster will be essential to the development and optimization of codes needed for both gravitational-wave (GW) data analysis as part of the Laser-Interferometer Gravitational-wave Observatory (LIGO) Scientific Collaboration, and GW source modeling for the physical interpretation of the detections. The equipment consists of a large computer cluster (56 nodes) with InfiniBand networking and 70 TB of usable storage. This cluster will incorporate three innovative Graphics-Programming-Unit nodes (using GPUs appropriate for scientific computing) which will be used as accelerators for special-purpose massively parallel computations. The cluster will be housed at a top-of-the-line HPC data center on the Northwestern University campus and will be operated and managed by an experienced team of HPC professionals led by one of the co-PIs. In more detail, the cluster will be used for GW research focused on binaries with two compact objects (neutron stars and/or black holes) in interdisciplinary collaborations between GW data analysts (members of the LIGO Scientific Collaboration), astrophysicists, and computer scientists. The goals are to optimize data searches for GW signals (through effective detector characterization), to extract as promptly as possible and accurately the physical properties of the signal sources (through continuous improvements of our parameter-estimation algorithms), and to advance the astrophysical interpretation of the discoveries so we can better understand the sources' origin and constrain theoretical models using our GW observations (through the development of state-of-the-art formation models in different environments and comparing predictions to data). The team is led by PI Kalogera and co-PI Rasio, who are well recognized for their significant impact in these research areas and for their innovative development of new computational tools for GW data analysis and astrophysical modeling of GW sources.