Unfolding in high energy physics represents the correction of measured spectra in data for the finite detector efficiency, acceptance, and resolution from the detector to particle level. Recent machine learning approaches provide unfolding on an event-by-event basis allowing to simultaneously unfold a large number of variables and thus to cover a wider region of the features that affect detector response. This study focuses on a simple comparison of commonly used methods in RooUnfold package to the machine learning package Omnifold.
@article{arxiv.2104.03036,
title = {Comparison of Machine Learning Approach to other Unfolding Methods},
author = {Petr Baron},
journal= {arXiv preprint arXiv:2104.03036},
year = {2021}
}