European Physical Journal C: Particles and Fields (Feb 2019)

Centrality and transverse momentum dependent suppression of $$\Upsilon (1S)$$ Υ(1S) and $$\Upsilon (2S)$$ Υ(2S) in p–Pb and Pb–Pb collisions at the CERN Large Hadron Collider

  • Captain R. Singh,
  • S. Ganesh,
  • M. Mishra

DOI
https://doi.org/10.1140/epjc/s10052-019-6646-2
Journal volume & issue
Vol. 79, no. 2
pp. 1 – 17

Abstract

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Abstract Deconfined QCD matter in heavy-ion collisions has been a topic of paramount interest for many years. Quarkonia suppression in heavy-ion collisions at the relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) experiments indicate the quark-gluon plasma (QGP) formation in such collisions. Recent experiments at LHC have given indications of hot matter effect in asymmetric p–Pb nuclear collisions. Here, we employ a theoretical model to investigate the bottomonium suppression in Pb–Pb at $$\sqrt{s_{NN}}=2.76$$ sNN=2.76 , 5.02 TeV, and in p–Pb at $$\sqrt{s_{NN}}=5.02$$ sNN=5.02 TeV center-of-mass energies under a QGP formation scenario. Our present formulation is based on an unified model consisting of suppression due to color screening, gluonic dissociation along with the collisional damping. Regeneration due to correlated $$Q\bar{Q}$$ QQ¯ pairs has also been taken into account in the current work. We obtain here the net bottomonium suppression in terms of survival probability under the combined effect of suppression plus regeneration in the deconfined QGP medium. We mainly concentrate here on the centrality, $$N_\text {part}$$ Npart and transverse momentum, $$p_{T}$$ pT dependence of $$\Upsilon (1S)$$ Υ(1S) and $$ \Upsilon (2S)$$ Υ(2S) states suppression in Pb–Pb and p–Pb collisions at mid-rapidity. We compare our model predictions for $$\Upsilon (1S)$$ Υ(1S) and $$\Upsilon (2S)$$ Υ(2S) suppression with the corresponding experimental data obtained at the LHC energies. We find that the experimental observations on $$p_t$$ pt and $$N_\text {part}$$ Npart dependent suppression agree reasonably well with our model predictions.