https://doi.org/10.1140/epjc/s10052-024-13136-3
Regular Article - Experimental Physics
Event-by-event comparison between machine-learning- and transfer-matrix-based unfolding methods
1
Kirchhoff-Institut für Physik, Universität Heidelberg, Heidelberg, Germany
2
LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris, France
3
Institut für Theoretische Physik, Universität Heidelberg, Heidelberg, Germany
a
mathias.backes@kip.uni-heidelberg.de
Received:
3
November
2023
Accepted:
18
July
2024
Published online:
3
August
2024
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 
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).
© The Author(s) 2024
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Funded by SCOAP3.
