Physics:Quantum data analysis/Experimental Uncertainties and Errors: Difference between revisions

From HandWiki Test
Clean Book label remnants and backlink spacing
Remove stray header marker from Quantum data page
 
(One intermediate revision by one other user not shown)
Line 1: Line 1:
{{Short description|Experimental Uncertainties and Errors in particle-physics data analysis}}
{{Quantum data backlink|Standard Model Measurements}}
{{Quantum article nav|previous=Physics:Quantum data analysis/Differential Cross Sections|previous label=Differential Cross Sections|next=Physics:Quantum data analysis/Particle Property Measurements|next label=Particle Property Measurements}}
{{Quantum article nav|previous=Physics:Quantum data analysis/Differential Cross Sections|previous label=Differential Cross Sections|next=Physics:Quantum data analysis/Particle Property Measurements|next label=Particle Property Measurements}}
{{Short description|Experimental Uncertainties and Errors in particle-physics data analysis}}V
{{Quantum data backlink|Standard Model Measurements}}
<div style="display:flex; gap:24px; align-items:flex-start; max-width:1200px;">
<div style="display:flex; gap:24px; align-items:flex-start; max-width:1200px;">



Latest revision as of 23:43, 23 May 2026

← Previous : Differential Cross Sections
Next : Particle Property Measurements →
Experimental Uncertainties and Errors is used in particle-physics data analysis to turn detector output, simulated samples, and theoretical models into quantitative physics results. In high-energy experiments the term is connected with event selection, calibration, uncertainty treatment, validation, and comparison with Standard Model or beyond-Standard-Model predictions. The analysis task is usually defined by the observable being measured or the signal being searched for. A robust workflow keeps raw detector information, reconstructed objects, simulated events, control samples, and statistical models traceable so that assumptions can be checked and systematic uncertainties can be propagated. In practice, experimental uncertainties and errors must be documented with selection definitions, units, binning choices, correction factors, and reproducible code or configuration.
Error creating thumbnail: File missing
Systematic uncertainties track how calibrations, models, and detector effects change a result.

Overview

Experimental Uncertainties and Errors is used in particle-physics data analysis to turn detector output, simulated samples, and theoretical models into quantitative physics results. In high-energy experiments the term is connected with event selection, calibration, uncertainty treatment, validation, and comparison with Standard Model or beyond-Standard-Model predictions.

Analysis role

The analysis task is usually defined by the observable being measured or the signal being searched for. A robust workflow keeps raw detector information, reconstructed objects, simulated events, control samples, and statistical models traceable so that assumptions can be checked and systematic uncertainties can be propagated.

Practical considerations

In practice, experimental uncertainties and errors must be documented with selection definitions, units, binning choices, correction factors, and reproducible code or configuration. This makes the result easier to compare across experiments and easier to reinterpret when improved simulations, calibrations, or theoretical predictions become available.[1]

Quality checks

For experimental uncertainties and errors, useful checks include closure tests on simulated samples, comparison with independent control regions, and stability tests under reasonable changes of selection, calibration, and binning. These checks help separate statistical fluctuations from analysis choices and detector effects.

Documentation

The page should record the definition of the objects being used, the data or simulation inputs, and the uncertainty model. That documentation is important because later measurements often reuse the same workflow with improved detector conditions or larger data sets.

See also

Table of contents (60 articles)

Index

Full contents

15. Machine Learning (1) Back to index

References

  1. "Review of Particle Physics". Physical Review D 110 (3): 030001. 2024. doi:10.1103/PhysRevD.110.030001. 
Author: Sergei V. Chekanov
Author: Claude Pruneau
Author: Harold Foppele

Source attribution: Physics:Quantum data analysis/Experimental Uncertainties and Errors