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

TL;DR

This paper compares traditional transfer-matrix-based unfolding methods with modern machine learning approaches, introducing a probabilistic single-event unfolding technique and evaluating its performance on simulated data.

Contribution

It presents a novel probabilistic single-event unfolding method for transfer-matrix algorithms and compares its effectiveness with machine learning-based unfolding techniques.

Findings

Transfer-matrix unfolding can be extended to probabilistic single-event distributions.

The proposed method is validated on toy models and pseudo-data.

Performance comparison shows strengths and limitations of both approaches.

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 $pp\rightarrow Z\gamma \gamma$ 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).