Supervised ML classification of neutrino events by interaction channel prior to energy reconstruction improves accuracy and sensitivity by 10-20% in simulated DUNE analyses while remaining robust to generator mismodeling.
Argon spectral function and neutrino interactions
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abstract
The argon spectral function is constructed and applied to neutrino-argon cross section computations in the plane wave impulse approximation with the Pauli blocking final state interaction effect taken into account. The approximations of the construction method are critically analyzed using the example of oxygen for which more detailed computations are available. An effective description of nucleus based on the information contained in a spectral function is proposed. It is demonstrated that its predictions are close to those obtained from the complete spectral function. The effective description can be easily applied in Monte Carlo event generators.
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hep-ph 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Improving Neutrino Oscillation Measurements through Event Classification
Supervised ML classification of neutrino events by interaction channel prior to energy reconstruction improves accuracy and sensitivity by 10-20% in simulated DUNE analyses while remaining robust to generator mismodeling.