Event-by-event comparison between machine-learning- and transfer-matrix-based unfolding methods

Abstract

Abstract The unfolding of detector effects is a key aspect 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. Traditionally, many analyses unfold detector effects using transfer-matrix-based algorithms, which are well established in low-dimensional unfolding. They yield an unfolded distribution of the total spectrum, together with its covariance matrix. This paper proposes a method to obtain probabilistic single-event unfolded distributions, 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 $$pp\rightarrow Z\gamma \gamma $$ p p → Z γ γ process. In both examples the performance is compared to the Machine-Learning-based single-event unfolding using an iterative approach with conditional invertible neural networks (IcINN).