COMPARISON OF MACHINE LEARNING APPROACH TO OTHER COMMONLY USED UNFOLDING METHODS

被引：1

作者：

Baron, Petr ^{[1
]}

机构：

[1] Palacky Univ, Fac Sci, Joint Lab Opt Palacky Univ & Inst Phys AS CR, 17 Listopadu 12, Olomouc 77146, Czech Republic

来源：

ACTA PHYSICA POLONICA B | 2021年 / 52卷 / 08期

关键词：

D O I：

10.5506/APhysPolB.52.863

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

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.

引用

页码：863 / 869

页数：7

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