Objective
Neutrino oscillation experiments are entering a new era of precision, utilizing 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 for oscillation experiments (see next section for details). A novel methodology to incorporate quantum-mechanical effects into the description of final state interactions, a domain typically modeled by semi-classical intra-nuclear cascades in neutrino event generators, will be introduced. To achieve this goal, the PI will pioneer the development of a new real-time Variational Monte Carlo algorithm, building upon the formalism employed to obtain the ANN nuclear-wave functions.
New tools will be developed to describe the high-energy region relevant for DUNE in which the degrees of freedom switch from nucleons and pions to partons. Finally, the PI will coordinate a pioneering effort to estimate the uncertainty of the theoretical predictions needed for oscillation analysis and to be able to discover new physics.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
46010 Valencia
Spain