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NOBEL PRIZE 2026

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NSFDirectorate for Mathematical and Physical Sciences

WoU-MMA: Operation of the HAWC Gamma Ray Observatory and US HAWC Data Analysis Center

Active
Institution
University of Maryland, College Park, MD
Principal investigator
Jordan A Goodman
Funding
$300K
Duration
2026–2027
Open portfolio

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Abstract

The operation and maintenance of the U.S. data archive and computing center for the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory are supported through this award. The archive, housed at the University of Maryland, stores and processes the enormous volume of scientific data collected by HAWC, a unique observatory located high on a mountain in Mexico that continuously monitors the sky for the universe’s most energetic light, known as gamma rays. By enabling discoveries about black holes, exploding stars, and other extreme cosmic environments, this work advances fundamental knowledge of the universe while strengthening U.S. leadership in data-intensive science. Students and postdoctoral researchers are trained in large-scale computing, data analysis, and advanced statistical and machine learning techniques that are broadly applicable in science, engineering, and industry. These activities promote the progress of science and help develop a highly skilled technical workforce that benefits the Nation. The continued operation of the more than 13-petabyte U.S. HAWC data archive and computing center is supported, together with engineering, technical, and operational activities required for the observatory. HAWC is a wide-field, high-duty-cycle observatory located at an altitude of 4,100 meters on Sierra Negra, Mexico, where extensive air showers produced by TeV gamma rays and cosmic rays are recorded continuously. Approximately 2 terabytes of raw data are acquired each day and are archived, processed, and distributed through the University of Maryland computing center. More than a decade of observations has been reprocessed using improved event reconstruction and analysis techniques, including machine learning, providing substantially enhanced sensitivity, angular resolution, and background rejection. These advances have enabled the detection and characterization of more than 100 very-high-energy gamma-ray sources, including TeV halos, micro-quasars, star-forming regions, and other astrophysical particle accelerators, while providing the long-term data set needed to investigate the origin of the highest-energy particles in the Galaxy. 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.

Research team

  • Jordan A GoodmanPI
  • Andrew J SmithCo-PI

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Directorate for Mathematical and Physical Sciences01002627DB NSF RESEARCH & RELATED ACTIVITParticle Astrophysics/Cosmic Puniversityadvancedcharacterizationcontinuouslyfieldreconstructionangularenergeticinvestigatewhere

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