Collective properties of hadrons in comparison of models prediction in pp collisions at 7 TeV

Analysis of the spectra of unidentified charged particles obtained by the CMS experiment in proton–proton collisions is reported in comparison with the simulation results of PYTHIA8.24 and EPOS-LHC models. The spectra obtained by the experiment were normalized to all non-single-diffractive (NSD) events using corrections for trigger and selection efficiency, acceptance, and branching ratios. The transverse-momentum (pT) spectra of the charged particles are measured in twelve equal bins of pseudorapidity (η) from 0.0 to 2.4 for pT from 0.1 to 2 GeV/c. The PYTHIA model reproduces the experimental data well in all bins of η especially in the region of high pT while the EPOS model predicts well in the intermediate pT regions. The intermediate regions where the EPOS model predicts well, broadens with increasing η.We used the Blast-wave model with Boltzmann–Gibbs statistics to study collective properties of the hadronic matter and for better comparison of the models’ prediction with the experimental data while determining the values of kinetic freeze-out temperature (T0) and transverse flow velocity (βT) for data and models. The values of T0 decrease with increasing η for data as well as for both the models. The transverse flow velocity has no clear trend with increasing η but a run through shows an increasing trend in the case of the data and the PYTHIA model but a decreasing trend in the case of the EPOS model. The multiplicity parameter N0 increases with increasing η and its values obtained by the fit function for the PYTHIA are closer to the ones obtained for data than the EPOS. It is concluded that none of the models completely describes the data in all bins of η over the entire pT range but the PYTHIA has better prediction than the EPOS model because the former has implied flow-like effects and formation of color string resulting from multiple hard sub-collisions between final and initial partons (color reconnection) from independent hard scatterings due to which the model predicts the data well.