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Unfolding in high energy physics represents the correction of measuredspectra in data for the finite detector efficiency, acceptance, and resolutionfrom the detector to particle level. Recent machine learning approaches provideunfolding on an event-by-event basis allowing to simultaneously unfold a largenumber of variables and thus to cover a wider region of the features thataffect detector response. This study focuses on a simple comparison of commonlyused methods in RooUnfold package to the machine learning package Omnifold.

Comparison of Machine Learning Approach to other Unfolding Methods