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Multiplicity dependence of (multi-)strange baryons in the canonical ensemble with phase shift corrections
The increase in strangeness production with charged particle multiplicity, as seen by the ALICE collaboration at CERN in p-p, p-Pb, and Pb-Pb collisions, is investigated in the hadron resonance gas model taking into account interactions among hadrons using S-matrix corrections based on known phase s...
Autores principales: | , , , |
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Lenguaje: | eng |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1103/PhysRevC.103.014904 http://cds.cern.ch/record/2730214 |
Sumario: | The increase in strangeness production with charged particle multiplicity, as seen by the ALICE collaboration at CERN in p-p, p-Pb, and Pb-Pb collisions, is investigated in the hadron resonance gas model taking into account interactions among hadrons using S-matrix corrections based on known phase shift analyses. Strangeness conservation is taken into account in the framework of the canonical strangeness ensemble. A very good description is obtained for the variation of the strangeness content in the final state as a function of the number of charged hadrons in the midrapidity region using the same fixed temperature value as obtained in the most central Pb-Pb collisions and with a fixed strangeness suppression factor γs=1. It is shown that the number of charged hadrons is linearly proportional to the volume of the system. For small multiplicities the canonical ensemble with local strangeness conservation restricted to midrapidity leads to a stronger suppression of (multi)strange baryons than seen in the data. This is compensated by introducing a global conservation of strangeness in the whole phase-space which is parametrized by the canonical correlation volume larger than the fireball volume at the midrapidity. The results on comparing the hadron resonance gas model with and without S-matrix corrections, are presented in detail. It is shown that the interactions introduced by the phase shift analysis via the S-matrix formalism are essential for a better description of the yields data. |
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