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.
Quark-hadron duality in neutrino scattering
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abstract
We present a phenomenological model of the quark-hadron transition in neutrino-nucleon scattering. Using recently extracted weak nucleon transition form factors, we investigate the extent to which local and global quark-hadron duality is applicable in the neutrino F_1, F_2 and F_3 structure functions, and contrast this with duality in electron scattering. Our findings suggest that duality works relatively well for neutrino-nucleon scattering for the F_2 and F_3 structure functions, but not as well for F_1. We also calculate the quasielastic, resonance and deep inelastic contributions to the Adler sum rule, and find it to be satisfied to within 10% for 0.5 < Q^2 < 2 GeV^2.
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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.