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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
The study evaluates and contrasts sophisticated and empirical model components in GENIE for pionless neutrino-argon interactions using recent MicroBooNE measurements.
SBND samples off-axis neutrino fluxes to provide a handle on cross-section and position-independent uncertainties for short-baseline neutrino physics, with public flux and covariance data.
citing papers explorer
-
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.
-
Benchmarking State-of-the-Art Theory and Empirical Models of Pionless Neutrino-Argon Scattering in GENIE
The study evaluates and contrasts sophisticated and empirical model components in GENIE for pionless neutrino-argon interactions using recent MicroBooNE measurements.
-
Sampling Off-Axis Neutrino Fluxes with the Short-Baseline Near Detector
SBND samples off-axis neutrino fluxes to provide a handle on cross-section and position-independent uncertainties for short-baseline neutrino physics, with public flux and covariance data.