Uncertainty components in profile likelihood fits

When a measurement of a physical quantity is reported, the total uncertainty is usually decomposed into statistical and systematic uncertainties. This decomposition is not only useful for understanding the contributions to the total uncertainty, but is also required to propagate these contributions...

Full description

Saved in:
Bibliographic Details
Published in:The European physical journal. C, Particles and fields Particles and fields, 2024-06, Vol.84 (6), p.593-15, Article 593
Main Authors: Pinto, Andrés, Wu, Zhibo, Balli, Fabrice, Berger, Nicolas, Boonekamp, Maarten, Chapon, Émilien, Kawamoto, Tatsuo, Malaescu, Bogdan
Format: Article
Language:English
Subjects:
Astronomy
Astrophysics and Cosmology
Decomposition
Elementary Particles
Hadrons
Heavy Ions
High energy physics
Measurement Science and Instrumentation
Nuclear Energy
Nuclear Physics
Parameter estimation
Physics
Physics and Astronomy
Quantum Field Theories
Quantum Field Theory
Quarks
Regular Article - Experimental Physics
Statistical analysis
Statistical methods
Statistical models
String Theory
Uncertainty
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:When a measurement of a physical quantity is reported, the total uncertainty is usually decomposed into statistical and systematic uncertainties. This decomposition is not only useful for understanding the contributions to the total uncertainty, but is also required to propagate these contributions in subsequent analyses, such as combinations or interpretation fits including results from other measurements or experiments. In profile likelihood fits, widely applied in high-energy physics analyses, contributions of systematic uncertainties are routinely quantified using “impacts,” which are not adequate for such applications. We discuss the difference between impacts and actual uncertainty components, and establish methods to determine the latter in a wide range of statistical models.
ISSN:1434-6052
1434-6044
1434-6052
DOI:10.1140/epjc/s10052-024-12877-5