KIP-Veröffentlichungen

The unfolding of detector effects is a key as- pect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been developed to provide novel features, e.g. high dimensionality or a probabilistic single-event unfolding based on generative neural networks. Tradi- tionally, many analyses unfold detector effects using transfer-matrix–based algorithms, which are well estab- lished in low-dimensional unfolding. They yield an un- folded distribution of the total spectrum, together with its covariance matrix. This paper proposes a method to obtain probabilistic single-event unfolded distribu- tions, together with their uncertainties and correlations,

for the transfer-matrix–based unfolding. The algorithm is first validated on a toy model and then applied to pseudo-data for the pp → Zγγ process. In both exam- ples the performance is compared to the single-event unfolding of the Machine-Learning–based Iterative cINN unfolding (IcINN).