Physics:Quantum data analysis/Event Measurements: Difference between revisions
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{{Short description|Event measurements in particle-physics data analysis}} | {{Short description|Event measurements in particle-physics data analysis}} | ||
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'''Event | '''Event Measurements''' is a topic in particle-physics data analysis. 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. 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. Event-level variables summarize the topology of a collision. Examples include invariant masses, scalar energy sums, angular separations, missing momentum, multiplicities, and quality requirements. | ||
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== Uncertainty propagation == | == 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> | 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> | ||
== Overview == | |||
'''Event Measurements''' 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, event measurements 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> | |||
=See also= | =See also= | ||
Latest revision as of 23:43, 23 May 2026
Event Measurements is a topic in particle-physics data analysis. 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. 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. Event-level variables summarize the topology of a collision. Examples include invariant masses, scalar energy sums, angular separations, missing momentum, multiplicities, and quality requirements.
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]
Overview
Event Measurements 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, event measurements 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.[4]
See also
Table of contents (60 articles)
Index
Full contents
References
- ↑ 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.
- ↑ "Review of Particle Physics". Physical Review D 110 (3): 030001. 2024. doi:10.1103/PhysRevD.110.030001.
Source attribution: Physics:Quantum data analysis/Event Measurements
