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The Lund Fragmentation Process for a Multi-gluon String According to the Area Law

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arxiv hep-ph/0106185 v1 pith:RARAEGYT submitted 2001-06-16 hep-ph

classification hep-ph
keywords areacurvestringfragmentationlundstateswilldirectrix
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The Lund Area Law describes the probability for the production of a set of colourless hadrons from an initial set of partons, in the Lund string fragmentation model. In this paper we will present a general method to implement the Area Law for a multi-gluon string state. The partonic states are in general given by a perturbative QCD cascade and are consequently defined only down to a cutoff in the energy momentum fluctuations. We will show that our method defines the states down to the hadronic mass scale inside an analytically calculable scenario. We will then show that there is a differential version of our process which is closely related to the generalised rapidity range \lambda, which has been used as a measure on the partonic states. We identify \lambda as the area spanned between the directrix curve (the curve given by the parton energy momentum vectors laid out in colour order, which determines the string surface) and the average curve (to be called the P-curve) of the stochastic X-curves (curves obtained when the hadronic energy-momentum vectors are laid out in rank order). Finally we show that from the X-curve corresponding to a particular stochastic fragmentation situation it is possible to reproduce the directrix curve (up to one starting vector and a set of sign choices, one for each hadron).

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Probing Partonic Evolution and Hadronization via Balance Functions and Correlations of Charmed Hadrons

    hep-ex 2025-07 conditional novelty 6.0 of 10

    PYTHIA 8.3 predicts charm balance functions for pp collisions at 13 TeV, showing a D0-dominant flavor balancing hierarchy and sensitivity to Lund string fragmentation parameters.

  2. Physics-informed neural network (PINN) modeling of charged particle multiplicity using the two-component framework in heavy-ion collisions: A comparison with data-driven neural networks

    hep-ph 2025-11 unverdicted novelty 4.0 of 10

    A PINN constrained by the two-component multiplicity model learns the hard-scattering fraction from Zr+Zr events and predicts N_ch more accurately than a data-driven NN on unseen Ru+Ru and Au+Au collisions.

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