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Revision as of 10:57, 22 May 2026
Book IV
Jets are collimated sprays of particles produced when high-energy quarks or gluons fragment into hadrons. In data analysis, jets are reconstructed objects rather than elementary particles: they depend on clustering algorithms, calibration, detector response, pileup mitigation, and analysis definitions. Jets are essential for QCD studies, top physics, Higgs measurements, and many searches for new physics. Jet algorithms cluster particles or detector objects using distance measures in momentum and angle. The algorithm choice and radius parameter define the measured object and must match the theory or simulation comparison. Jet energy scale, resolution, flavor response, pileup, and tagging efficiency are major sources of systematic uncertainty.
Jet reconstruction
Jet algorithms cluster particles or detector objects using distance measures in momentum and angle. The algorithm choice and radius parameter define the measured object and must match the theory or simulation comparison.[1]
Calibration and uncertainties
Jet energy scale, resolution, flavor response, pileup, and tagging efficiency are major sources of systematic uncertainty. Control samples and simulation are used to calibrate jets and estimate their uncertainties.[2][3]
Physics role
Jets reveal quark and gluon dynamics, hadronic decays of heavy particles, missing-energy backgrounds, and event topology. Substructure methods can identify boosted particle decays inside a single large-radius jet.[4]
Overview
Jets 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, jets 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.[5]
See also
Table of contents (60 articles)
Index
Full contents
References
- ↑ "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.
- ↑ Sjostrand, Torbjorn; Mrenna, Stephen; Skands, Peter (2008). "A brief introduction to PYTHIA 8.1". Computer Physics Communications 178 (11): 852-867. doi:10.1016/j.cpc.2008.01.036.
- ↑ "Review of Particle Physics". Physical Review D 110 (3): 030001. 2024. doi:10.1103/PhysRevD.110.030001.
Source attribution: Physics:Quantum data analysis/Jets
