Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods

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 $pp\to Z{\hat{I}}^3{\hat{I}}^3$ 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).

25 pages, 13 figures, corresponds to the published version