Developing an AI-assisted, Full-band, all Digital Trigger for Radio Neutrino Astronomy
- Institution
- University of California-Irvine, CA
- Principal investigator
- Steven W Barwick
- Funding
- $377K
- Duration
- 2024–2026
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Abstract
The primary science mission for radio-based high energy neutrino technology is to search for astrophysical sources of neutrinos with extremely high energies, at least 100,000 times higher than the highest energy particles created by the strongest terrestrial particle accelerators. Observations at this extreme provide insight into the nature of the most extreme particle accelerators in the Universe and may point to the long-sought sources of the highest energy cosmic rays. The NSF-supported ARIANNA collaboration pioneered the development of the surface station architecture for radio-based neutrino astronomy and is thus ideally placed to contribute to multi-messenger science, providing critical capabilities such as excellent pointing resolution and measurement of the neutrino cross-section. This research will create the next generation of trigger technology and will revitalize the successful design and operation of surface station experiments. The project will provide training in both hardware and software to students at both graduate and undergraduate levels, including under-represented groups and women. Such experience will be a valuable asset for future workforce placement. The ARIANNA design relies on the observation of radio emission from neutrino interactions in cold transparent Antarctic ice by radio antennas buried only a few meters below the snow surface, but previously used a very simple scheme to trigger the detector. The next generation of trigger technology will exploit the unique ”chirped” features of the waveforms produced by these shallow antennas by developing an all-digital trigger, so that trigger schemes can access the full data records before creating their trigger event. This new trigger hardware will implement AI tools such as Deep Learning, which work best with unfiltered data directly imported from high speed digitizers. The work will continue a fruitful collaboration with Uppsala University (Sweden). 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.
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