Two-component model in quantum statistical framework compared with multiplicity distributions in proton-proton collisions at energies up to $\sqrt {s}$ = 7 TeV

Details Two-component model in quantum statistical framework compared with multiplicity distributions in proton-proton collisions at energies up to $\sqrt {s}$ = 7 TeV Two-component model in quantum statistical framework compared with multiplicity distributions in proton-proton collisions at energies up to $\sqrt {s}$ = 7 TeV

2012-02-20

arxiv.org/abs/1202.4226v1

Physics

High Energy Physics

High Energy Physics - Phenomenology

6 pages 7 figures. Published in Physics Letters B. arXiv admin note: substantial text overlap with arXiv:1107.1291

Scientific paper

10.1016/j.physletb.2011.10.033

Proton-proton collisions at new high energies ($\sqrt {s} =$ 2.36 and 7 TeV) at LHC resulted into greater mean multiplicities ($$) of charged particles in the mid-rapidity region than estimated ones by different models and event generators. Another significant observation in multiplicity data is the change in slope in the distribution of primary charged hadrons in symmetric pseudorapidity interval $|\eta|<$2.4. The change is most prominent with data at $\sqrt{s} = 7$ TeV. These new observations merit further studies. We consider a two-component model of particle production to analyze multiplicity distributions of charged hadrons from proton-proton collisions at centre-of-mass energies $\sqrt{s} = $ 0.9, 2.36 and 7 TeV in symmetric pseudorapidity intervals $|\eta|$ of increasing width around the centre-of-mass pseudorapidity $\eta_{cm} = 0$. The model, based on quantum statistical (QS) formalism, describes multiplicity distribution by convolution of a Negative Binomial Distribution (NBD), representing a chaotic component, and a Poisson Distribution (PD), representing a coherent component of particle productions. The behaviour of characteristic parameters of the model is followed by the LHC data, while a scaling law, involving information entropy in quantum statistical viewpoint and derived as a function of chaotic multiplicity obtained from the two-component model, is not obeyed by the data, satisfactorily. An attempt to match the measured multiplicity distributions and suggested convolutions with values of characteristic parameters extracted from the data confirms disagreement between the data and the model.

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