Physics:Quantum data analysis/Tracking: Difference between revisions
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'''Tracking''' reconstructs the trajectories of charged particles from detector hits, usually inside a magnetic field. Track curvature gives momentum, while hit patterns and fitted impact parameters help determine charge, vertices, lifetimes, and particle identity. Tracking is one of the most important measurements in collider experiments because it anchors event reconstruction at high spatial precision.<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_track_reconstruction_yellow.png|thumb|280px| | [[File:Quantum_data_analysis_track_reconstruction_yellow.png|thumb|280px|Tracking represented as charged-particle trajectories in a magnetic detector.]] | ||
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== Track reconstruction == | |||
Tracking algorithms associate hits across detector layers and fit them to trajectories. Pattern recognition must handle detector noise, inefficiencies, multiple scattering, overlapping events, and secondary interactions.<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> | |||
== Momentum and vertices == | |||
A charged particle's curvature in a magnetic field determines its transverse momentum. Tracks are also fitted to primary and secondary vertices, enabling heavy-flavor tagging and lifetime measurements.<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> | |||
== Performance == | |||
Tracking performance is described by efficiency, fake rate, momentum resolution, impact-parameter resolution, and alignment uncertainties. These quantities are measured with control samples and propagated into physics results.<ref name="atlasdet">{{cite journal |collaboration=ATLAS Collaboration |title=The ATLAS Experiment at the CERN Large Hadron Collider |journal=Journal of Instrumentation |volume=3 |pages=S08003 |year=2008 |doi=10.1088/1748-0221/3/08/S08003}}</ref><ref name="cmsdet">{{cite journal |collaboration=CMS Collaboration |title=The CMS experiment at the CERN LHC |journal=Journal of Instrumentation |volume=3 |pages=S08004 |year=2008 |doi=10.1088/1748-0221/3/08/S08004}}</ref> | |||
=See also= | =See also= | ||
Revision as of 20:58, 19 May 2026
Tracking reconstructs the trajectories of charged particles from detector hits, usually inside a magnetic field. Track curvature gives momentum, while hit patterns and fitted impact parameters help determine charge, vertices, lifetimes, and particle identity. Tracking is one of the most important measurements in collider experiments because it anchors event reconstruction at high spatial precision.[1]
Track reconstruction
Tracking algorithms associate hits across detector layers and fit them to trajectories. Pattern recognition must handle detector noise, inefficiencies, multiple scattering, overlapping events, and secondary interactions.[1]
Momentum and vertices
A charged particle's curvature in a magnetic field determines its transverse momentum. Tracks are also fitted to primary and secondary vertices, enabling heavy-flavor tagging and lifetime measurements.[2]
Performance
Tracking performance is described by efficiency, fake rate, momentum resolution, impact-parameter resolution, and alignment uncertainties. These quantities are measured with control samples and propagated into physics results.[3][4]
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.
- ↑ "The ATLAS Experiment at the CERN Large Hadron Collider". Journal of Instrumentation 3: S08003. 2008. doi:10.1088/1748-0221/3/08/S08003.
- ↑ "The CMS experiment at the CERN LHC". Journal of Instrumentation 3: S08004. 2008. doi:10.1088/1748-0221/3/08/S08004.
Author: Sergei V. Chekanov
Author: Claude Pruneau
Author: Harold Foppele
Source attribution: Physics:Quantum data analysis/Tracking
