Machine learning methods were explored for particle identification (PID) in the SuperFGD detector, which part of the recent upgrade to the T2K near detector. Baseline models were established to provide benchmarks, followed by a classical neural network where feature engineering was applied to improve separation. A transformer-based architecture was then developed, with data augmentation,...
We present a dual application of conditional normalizing flows to (i) accelerate Monte Carlo sampling of exclusive neutrino–nucleus cross-sections and (ii) model uncertainty distributions within bayesian fits of systematics, illustrated on the T2K near-detector fit. Although aimed at different applications and uses, both efforts share a nearly identical implementation: an iterative training...
