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{{Short description|Comparing Data and Theory in particle-physics data analysis}} | {{Short description|Comparing Data and Theory in particle-physics data analysis}} | ||
{{Quantum data backlink|General Statistical Methods and Data Visualisation}} | {{Quantum data backlink|General Statistical Methods and Data Visualisation}} | ||
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'''Comparing Data and Theory''' 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, comparing data and theory must be documented with selection definitions, units, binning choices, correction factors, and reproducible code or configuration.</div> | |||
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== Overview == | |||
'''Comparing Data and Theory''' 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, comparing data and theory 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.<ref name="pdg-data">{{cite journal |collaboration=Particle Data Group |title=Review of Particle Physics |journal=Physical Review D |volume=110 |issue=3 |pages=030001 |year=2024 |doi=10.1103/PhysRevD.110.030001}}</ref> | |||
== Quality checks == | |||
For comparing data and theory, 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= | =See also= | ||
Latest revision as of 23:42, 23 May 2026
Comparing Data and Theory 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, comparing data and theory must be documented with selection definitions, units, binning choices, correction factors, and reproducible code or configuration.
Overview
Comparing Data and Theory 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, comparing data and theory 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 comparing data and theory, 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
Foundations and experiments
Analysis, measurements and discovery
Full contents
1. Introduction to Particle Physics (5) Back to index
2. Nuts and Bolts (4) Back to index
3. Overview of Particle Collision Experiments (3) Back to index
4. Collision Kinematics and Complex Observables (8) Back to index
5. Basic Measurements (4) Back to index
6. Data Acquisition Electronics and Systems (1) Back to index
7. Event Reconstruction (3) Back to index
8. Monte Carlo Simulations (4) Back to index
9. General Statistical Methods and Data Visualisation (6) Back to index
10. Standard Model Measurements (6) Back to index
11. Searches for New Physics (4) Back to index
12. Data Processing Techniques (4) Back to index
13. Fit Optimisation Techniques (4) Back to index
14. Advanced Data Comprehension Techniques (3) Back to index
15. Machine Learning (1) Back to index
60. Machine Learning
References
- ↑ "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/Comparing Data and Theory
