Speaker
Sebastian Pitz
(LPNHE Paris)
Description
Machine learning enables unbinned unfolding with per-event posteriors, but a measurement is only as good as its error bars. We dissect the uncertainty budget of conditional flow matching unfolding for WZ production. Bootstraps propagate the statistics of the training sample and, by reweighting, of the data. We show how weight sampling from a Bayesian neural network relates to the spread of retraining ensembles. This turns generative unfolding into a measurement tool with a transparent error budget.