Speaker
Mathias El Baz
Description
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 procedure that refines a conditional flow to match high dimensional conditional distributions. We describe the common framework, task-specific adaptations, and the resulting gains compared with Monte Carlo techniques currently used for these tasks.
