Collaborative Research: A Data Challenge for the Next Generation of Ground-Based Gravitational Wave Detectors

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The National Science Foundation’s Laser Interferometer Gravitational Wave Observatory (LIGO) has begun a new era in the exploration of the Universe. Scientists can now use gravitational waves, ripples in the fabric of spacetime, to explore distant objects like black holes and neutron stars. As LIGO continues to explore the universe, scientists are making plans for the next generation of gravitational-wave observatories; observatories that can see every black hole merger in the universe and have the potential to reveal the physics that governs the behavior of dense matter. In the United States, the community has been developing a design for a detector called Cosmic Explorer and Europe is proposing a complementary observatory known as the Einstein Telescope. Achieving the scientific potential of these observatories requires advanced algorithms and computational techniques that need to be developed now, so these algorithms are ready when the detectors begin exploring the gravitational-wave sky in the 2030s. These skills that students will learn developing these technologies will advance the competitiveness of the U.S. STEM workforce. This award will also support an effort to bring high-performance computing to school districts in rural Pennsylvania and promote STEM education in K-12 schools.A series of progressively more difficult data challenges will be created to confront the data-analysis hurdles presented by the next generation of gravitational-wave detectors. These challenges will: (i) inform the progress that would need to be made in the development of new algorithms for efficient detection and parameter inference, (ii) help estimate the computational resources required to fully exploit the science potential of next-generation detectors and (iii) build and engage a community of researchers that is ready to explore the Universe with this new observational window. This award will provide an opportunity to determine if science results from the signal-rich data of future detectors can be reliably extracted and stimulate research in the development of new analysis and inference algorithms that can deal with overlapping multiple signal types and strengths, of varying duration and cadence, all buried in data with non-stationarities and gaps.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.