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

Project description

Ultra-high energy neutrinos for increased detection rates

Detecting ultra-high energy neutrinos (UHE, E > 10^17 eV) represents a major breakthrough in astroparticle physics, with radio detection being the only available method. However, due to their low flux, even large facilities like the planned IceCube-Gen2 neutrino observatory at the South Pole may only detect a few events annually. The ERC-funded NuRadioOpt project aims to enhance UHE neutrino detectors by doubling the detection rate and improving event quality through deep learning and differential programming. It will replace traditional threshold-based triggers with neural networks, enabling higher detection rates without substantial hardware costs. The project will optimise the determination of neutrino direction and energy, which aligns perfectly with the goals of the envisioned IceCube-Gen2 facility.

Objective

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 detectors with neural networks, increasing the detection rate of UHE neutrinos by a factor of two at negligible additional hardware costs. I will perform an end-to-end optimization using differential programming and deep learning to improve the determination of the neutrino direction and energy.
My previous work on developing state-of-the-art MC simulation codes, my experience in data analysis, designing reconstruction algorithms and deep learning, and my leadership role in IceCube-Gen2 will enable this ERC project.
The timing of this project is perfect for influencing IceCube-Gen2 - the largest facility for astroparticle physics with neutrinos for the next decade - whose construction is planned to start in 2027. With this ERC project, IceCube-Gen2 will be able to expedite the discovery of UHE neutrino fluxes by up to a factor of five, see sources from deeper in our Universe increasing the observable volume by a factor of three, and measure the neutrino-nucleon cross-section at EeV energies with 3x smaller uncertainty. Hence, NuRadioOpt will substantially increase the capabilities of future observatories for UHE neutrinos.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2023-STG

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Host institution

UPPSALA UNIVERSITET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 738 721,00
Address
VON KRAEMERS ALLE 4
751 05 Uppsala
Sweden

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Region
Östra Sverige Östra Mellansverige Uppsala län
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 738 721,00

Beneficiaries (1)

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