Physics:Quantum data analysis/Event Measurements: Difference between revisions
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'''Event measurements''' are the reconstructed quantities extracted from a single collision or from a selected ensemble of collisions. They include object momenta, charges, particle-identification values, vertices, missing momentum, event shapes, multiplicities, trigger decisions, and quality flags. These measurements are the immediate inputs to selections, histograms, fits, and physics interpretations.<ref name="leo">{{cite book |last=Leo |first=William R. |title=Techniques for Nuclear and Particle Physics Experiments |publisher=Springer |year=1994 |isbn=978-3-540-57280-0}}</ref> | |||
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[[File:Quantum_data_analysis_event_measurements_yellow.png|thumb|280px|Event | [[File:Quantum_data_analysis_event_measurements_yellow.png|thumb|280px|Event measurements represented as reconstructed quantities from a collision event.]] | ||
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== From signals to objects == | |||
Detector signals are converted into reconstructed objects such as tracks, clusters, jets, leptons, photons, vertices, and missing transverse momentum. Each object carries calibration, resolution, and identification information.<ref name="leo">{{cite book |last=Leo |first=William R. |title=Techniques for Nuclear and Particle Physics Experiments |publisher=Springer |year=1994 |isbn=978-3-540-57280-0}}</ref> | |||
== Event-level variables == | |||
Event-level variables summarize the topology of a collision. Examples include invariant masses, scalar energy sums, angular separations, missing momentum, multiplicities, and quality requirements.<ref name="pdg2024">{{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> | |||
== Uncertainty propagation == | |||
Event measurements must propagate detector uncertainties and correlations into final distributions. Object-level calibrations can affect selections, background estimates, and fitted parameters.<ref name="cowan">{{cite book |last=Cowan |first=Glen |title=Statistical Data Analysis |publisher=Oxford University Press |year=1998 |isbn=978-0-19-850156-5}}</ref> | |||
=See also= | =See also= | ||
Revision as of 20:58, 19 May 2026
Event measurements are the reconstructed quantities extracted from a single collision or from a selected ensemble of collisions. They include object momenta, charges, particle-identification values, vertices, missing momentum, event shapes, multiplicities, trigger decisions, and quality flags. These measurements are the immediate inputs to selections, histograms, fits, and physics interpretations.[1]
From signals to objects
Detector signals are converted into reconstructed objects such as tracks, clusters, jets, leptons, photons, vertices, and missing transverse momentum. Each object carries calibration, resolution, and identification information.[1]
Event-level variables
Event-level variables summarize the topology of a collision. Examples include invariant masses, scalar energy sums, angular separations, missing momentum, multiplicities, and quality requirements.[2]
Uncertainty propagation
Event measurements must propagate detector uncertainties and correlations into final distributions. Object-level calibrations can affect selections, background estimates, and fitted parameters.[3]
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
- ↑ 1.0 1.1 Leo, William R. (1994). Techniques for Nuclear and Particle Physics Experiments. Springer. ISBN 978-3-540-57280-0.
- ↑ "Review of Particle Physics". Physical Review D 110 (3): 030001. 2024. doi:10.1103/PhysRevD.110.030001.
- ↑ Cowan, Glen (1998). Statistical Data Analysis. Oxford University Press. ISBN 978-0-19-850156-5.
Author: Sergei V. Chekanov
Author: Claude Pruneau
Author: Harold Foppele
Source attribution: Physics:Quantum data analysis/Event Measurements
