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

Oct 25, 2023
25 pages
Published in:
  • Eur.Phys.J.C 84 (2024) 8, 770
  • Published: Aug 3, 2024
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Abstract: (Springer)
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 ppZγγpp\rightarrow Z\gamma \gamma 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).
Note:
  • 25 pages, 13 figures, corresponds to the published version