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Neutrino Uncertainty Quantification using Neural Networks

CORDIS · observation · Publication date unknown

ing cutting-edge technologies and capabilities to offer unprecedented insights The ERC project NUQNET aims to enhance the prediction accuracy of neutrino-nucleus interactions by implementing advanced machine learning techniques. This endeavor involves incorporating predictive errors, a critical step necessary to meet the requirements and fully unlock the discovery potential of upcoming neutrino oscillation experiments. Presently, there is no theoretical method capable of consistently describing neutrino interactions across the wide energy spectrum investigated in neutrino experiments. Moreover, existing models in the market rely either on some approximations or on semi-phenomenological approaches, making it challenging to rigorously assess the theoretical uncertainty. This uncertainty must be meticulously propagated throughout the analysis to extract precise oscillation parameters. The PI plans to go beyond the limitations of traditional many-body techniques by using ANN architectures to represent the nuclear wave functions and obtain the spectral function of several nuclei relevant

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recordType

award

status

SIGNED

region

EU

value

1488481

unit

EUR