Physics:Quantum data analysis/Event Flows: Difference between revisions
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{{Short description|Event | {{Short description|Event-flow observables in particle-collision data analysis}} | ||
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'''Event flows''' describe collective patterns in the angular distribution of particles produced in a collision. The term is especially important in heavy-ion physics, where anisotropic flow coefficients quantify how the collision geometry and medium response shape final-state particle emission. Flow observables are also useful as event-shape variables in broader collider analyses.<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> | |||
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[[File:Quantum_data_analysis_event_flows_yellow.png|thumb|280px|Event | [[File:Quantum_data_analysis_event_flows_yellow.png|thumb|280px|Event-flow observables represented as collective angular patterns.]] | ||
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== Flow coefficients == | |||
Azimuthal distributions can be expanded in Fourier coefficients such as directed, elliptic, and triangular flow. These coefficients summarize collective angular structure in a way that can be compared across centrality, momentum, and particle species.<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> | |||
== Measurement methods == | |||
Flow measurements use event-plane, scalar-product, cumulant, and correlation techniques. Each method has different sensitivity to nonflow correlations, detector acceptance, and event-by-event fluctuations.<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> | |||
== Interpretation == | |||
In heavy-ion collisions, flow is interpreted as evidence for collective behavior and transport properties of strongly interacting matter. In smaller systems, flow-like signals require careful comparison with jets, resonance decays, and other correlations.<ref name="alicedet">{{cite journal |collaboration=ALICE Collaboration |title=The ALICE experiment at the CERN LHC |journal=Journal of Instrumentation |volume=3 |pages=S08002 |year=2008 |doi=10.1088/1748-0221/3/08/S08002}}</ref> | |||
=See also= | =See also= | ||
Revision as of 20:57, 19 May 2026
Event flows describe collective patterns in the angular distribution of particles produced in a collision. The term is especially important in heavy-ion physics, where anisotropic flow coefficients quantify how the collision geometry and medium response shape final-state particle emission. Flow observables are also useful as event-shape variables in broader collider analyses.[1]
Flow coefficients
Azimuthal distributions can be expanded in Fourier coefficients such as directed, elliptic, and triangular flow. These coefficients summarize collective angular structure in a way that can be compared across centrality, momentum, and particle species.[1]
Measurement methods
Flow measurements use event-plane, scalar-product, cumulant, and correlation techniques. Each method has different sensitivity to nonflow correlations, detector acceptance, and event-by-event fluctuations.[2]
Interpretation
In heavy-ion collisions, flow is interpreted as evidence for collective behavior and transport properties of strongly interacting matter. In smaller systems, flow-like signals require careful comparison with jets, resonance decays, and other correlations.[3]
See also
Table of contents (60 articles)
Index
Full contents
References
- ↑ 1.0 1.1 "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.
- ↑ "The ALICE experiment at the CERN LHC". Journal of Instrumentation 3: S08002. 2008. doi:10.1088/1748-0221/3/08/S08002.
Source attribution: Physics:Quantum data analysis/Event Flows
