Comparison of machine learning approach to other commonly used unfolding methods

被引：0

作者：

Baron P. ^{[1
]}

机构：

[1] Joint Laboratory of Optics of Palacký University, Institute of Physics AS CR, Faculty of Science, Palacký University, 17. listopadu 12, Olomouc

来源：

Acta Phys Pol B | / 8卷 / 863-869期

关键词：

Machine learning;

D O I：

10.5506/APHYSPOLB.52.863

中图分类号：

学科分类号：

摘要：

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. © 2021 Jagellonian University. All rights reserved.

引用

页码：863 / 869

页数：6

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