Cross sections and multiparticle production at supercollider energies in the dual parton model
Conference
·
· AIP Conference Proceedings (American Institute of Physics); (United States)
OSTI ID:5643824
- Fachbereich Physik, Universitaet Leipzig, Leipzig (Germany)
The dual parton model (DPM) describes soft and semihard multiparticle production and treats diffractive processes for the first time in a consistent way. The model is formulated in the form of a Monte-Carlo event generator, DTUJET for hadron-hadron collisions at collider energies. The uncertainties in the model predictions in the TeV energy range due to the unknown parton structure functions at [ital x][le]0.02 is explored. The behaviour of the model studied in the forward fragmentation region, which is especially relevant for the interaction of Cosmic Rays.
- OSTI ID:
- 5643824
- Report Number(s):
- CONF-9206284--
- Conference Information:
- Journal Name: AIP Conference Proceedings (American Institute of Physics); (United States) Journal Volume: 276:1
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
662340* -- Hadron Interactions-- (1992-)
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
CALCULATION METHODS
COMPOSITE MODELS
CROSS SECTIONS
DATA
ELEMENTARY PARTICLES
FERMIONS
FIELD THEORIES
HADRON-HADRON INTERACTIONS
INFORMATION
INTERACTIONS
MATHEMATICAL MODELS
MONTE CARLO METHOD
MULTIPLE PRODUCTION
MULTIPLICITY
NUMERICAL DATA
PARTICLE INTERACTIONS
PARTICLE MODELS
PARTICLE PRODUCTION
PARTON MODEL
POSTULATED PARTICLES
QUANTUM CHROMODYNAMICS
QUANTUM FIELD THEORY
QUARKS
SCALING LAWS
THEORETICAL DATA
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
CALCULATION METHODS
COMPOSITE MODELS
CROSS SECTIONS
DATA
ELEMENTARY PARTICLES
FERMIONS
FIELD THEORIES
HADRON-HADRON INTERACTIONS
INFORMATION
INTERACTIONS
MATHEMATICAL MODELS
MONTE CARLO METHOD
MULTIPLE PRODUCTION
MULTIPLICITY
NUMERICAL DATA
PARTICLE INTERACTIONS
PARTICLE MODELS
PARTICLE PRODUCTION
PARTON MODEL
POSTULATED PARTICLES
QUANTUM CHROMODYNAMICS
QUANTUM FIELD THEORY
QUARKS
SCALING LAWS
THEORETICAL DATA