Flattenicity as "centrality" estimator in p-Pb collisions simulated with PYTHIA 8.312 Angantyr
Abstract
In this paper, a "centrality" estimator based on flattenicity () is studied in proton-led (p-Pb) collisions at TeV using PYTHIA 8 Angantyr. Although Angantyr is still under development, the existing implementation is enough to study the particle production in systems where medium effects are absent. Firstly, ALICE data on pseudorapidity distributions as a function of the forward multiplicity (V0M), as well as transverse momentum distributions of identified particles in non-single diffractive p-Pb collisions, are compared with Angantyr. Secondly, the average number of binary nucleon-nucleon () collisions for different "centrality" estimators are compared. The studies include the following "centrality" estimators: V0M, and midrapidity multiplicity (CL1). On one hand, the "centrality" dependence of for the selection shows the smallest deviations ( %) with respect to that obtained using impact parameter ; on the other hand, the V0M and CL1 yield huge deviations (up to a factor 2) with respect to the results using . The particle ratios and nuclear modification factors () as a function of are also studied. The proton-to-pion ratio exhibits a flow-like peak at intermediate (2-8 GeV/) with little or no "centrality" dependence for V0M, and selections. The kaon-to-pion ratio as a function of is "centrality" independent for the same selections. On the contrary, for the CL1 class the ratios exhibit the typical behaviour associated with hard physics. Regarding , a peak at intermediate ( GeV/) for different particle species is observed when the "centrality" is obtained with or . The observed features diminish for the selections based on V0M and CL1.
Cite
@article{arxiv.2407.07724,
title = {Flattenicity as "centrality" estimator in p-Pb collisions simulated with PYTHIA 8.312 Angantyr},
author = {Antonio Ortiz and Gyula Bencedi and Feng Fan},
journal= {arXiv preprint arXiv:2407.07724},
year = {2024}
}
Comments
8 pages, 4 figures