SOURCE-LINKED INTELLIGENCE

Optimization of Radio Detectors of Ultra-High-Energy Neutrinos through Deep Learning and Differential Programming

CORDIS · observation · Publication date unknown

Optimization of Radio Detectors of Ultra-High-Energy Neutrinos through Deep Learning and Differential Programming Detection of neutrinos at ultra-high energies (UHE, E >10^17eV) would be one of the most important breakthroughs in astroparticle physics in the 21st century and would open a new window to the most violent phenomena in our universe. Radio detection remains the only viable technique at these energies. However, owing to the expected small flux of UHE neutrinos, the detection rate will be small, with just a handful of events per year, even for large future facilities like the IceCube-Gen2 neutrino observatory at the South Pole. In this project, I will enhance the science capabilities of UHE neutrino detectors substantially by increasing the detection rate of neutrinos and improving the quality of each detected event, using recent advances in deep learning and differential programming. I will replace the threshold-based trigger foreseen for future

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1738721

unit

EUR