ACED: Physics-informed Geometric Deep Learning for Astrophysical Neutrino Reconstruction i — NSF Award to Georgia Tech Research Co
Neutrinos are unique messengers, carrying information about the universe's most energetic astrophysical phenomena. Over the past decade, the IceCube Neutrino Observatory at the South Pole has made key discoveries by detecting high-energy neutrinos and identifying two active galaxies as neutrino sources. However, sub-Te
| Award title | ACED: Physics-informed Geometric Deep Learning for Astrophysical Neutrino Reconstruction i |
|---|---|
| Award ID | 2435957 |
| Awardee | Georgia Tech Research Corporation |
| City | ATLANTA |
| State | GA |
| Amount obligated | $487,503 |
| Principal investigator | Pan Li |
| Program | ACED-Accl Comp Enabled Sci Dis |
| Start date | 07/01/2025 |
| Abstract | Neutrinos are unique messengers, carrying information about the universe's most energetic astrophysical phenomena. Over the past decade, the IceCube Neutrino Observatory at the South Pole has made key discoveries by detecting high-energy neutrinos and identifying two active galaxies as neutrino sources. However, sub-TeV neutrinos (10–1000 GeV) remain a largely unexplored frontier with the potential to significantly expand our observation of the universe. This project leverages advanced artificia |
| Source | NSF Awards |
$799/mo
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