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Cornell partners in NSF grant for astrophysics institute

The (CAC) is among 10 collaborators awarded a $2.8 million grant from the ³Ô¹ÏÍøÕ¾ Science Foundation to develop the concept for a Scalable Cyberinfrastructure Institute for Multi-Messenger Astrophysics.

Adam Brazier, a computational scientist with CAC, is the technical lead on the project, which is being led by the University of Wisconsin, Milwaukee.

Multi-messenger astrophysics combines observations of electromagnetic waves (including light), gravitational waves and particles to understand some of the most extreme events in the universe. The goal of the institute, called , is to develop algorithms, databases and computing and networking cyberinfrastructure to help scientists interpret these multi-messenger observations.

“We’re very used to working collaboratively with researchers to understand their requirements and turn them into robust cyberinfrastructure that serves their needs,” said Brazier, who works closely with the Department of Astronomy in the College of Arts and Sciences and will set the technical direction and technical priorities for SCIMMA in his role.

“With ongoing improvements at the Laser Interferometer Gravitational-Wave Observatory and the upcoming commencement of Large Synoptic Survey Telescope operations, cyberinfrastructure specialists working hand in hand with astronomers are key to the successful growth of the field,” said , the David C. Duncan Professor in the Physical Sciences and chair of astronomy.

Brazier’s involvement with SCIMMA builds on his work with the , which monitors pulses from rotating neutron stars in order to detect the gravitational waves emitted from the supermassive black hole binaries, believed to be at the center of many galaxies.

CAC provides high-performance computing and cloud computing services to the Cornell community and beyond. The code for SCIMMA will be developed and tested on CAC’s $7.1 million, NSF-funded project – a combined cloud that allows multiple institutions to aggregate, share and analyze very large datasets.

The two-year SCIMMA conceptualization project began in September 2019.

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