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A study of charged-particle multiplicity distribution in high energy p-O collisions

Yuri N. Lima, Lucas J. F. Silva, Andre V. Giannini, Marcelo G. Munhoz

Abstract

This study investigates the multiplicity distribution of charged particles generated in $p$-O collisions, employing Pythia (Angantyr) and $k_T$-factorization approach. Oxygen nucleus configurations are sampled using a $α$-cluster model to evaluate both formalisms and assess how initial nucleus configuration influences the properties of the produced final states. Results obtained through clustering are systematically compared to those derived from the Woods-Saxon nuclear distribution. The analysis encompasses various pseudorapidity intervals ($|η|<$ 0.5, 1.0, 2.0, 3.0) and center-of-mass energies ($\sqrt{s}=$ 2.36, 5.02, 7.0, 13.0 TeV). Based on the resulting distributions, we examine the KNO scaling effect and fit the distributions with the double NBD model for parameterization, aiming to accurately characterize the observed results and elucidate contributions from both soft and semi-hard processes. Our results indicate that different geometric descriptions of the oxygen nucleus project significantly different multiplicities of charged particles, especially for large multiplicities and higher pseudorapidity. We also observed that multiplicity of charged particles calculated with Pythia reveals significantly different behavior from that calculated with $k_T$-factorization.

A study of charged-particle multiplicity distribution in high energy p-O collisions

Abstract

This study investigates the multiplicity distribution of charged particles generated in -O collisions, employing Pythia (Angantyr) and -factorization approach. Oxygen nucleus configurations are sampled using a -cluster model to evaluate both formalisms and assess how initial nucleus configuration influences the properties of the produced final states. Results obtained through clustering are systematically compared to those derived from the Woods-Saxon nuclear distribution. The analysis encompasses various pseudorapidity intervals ( 0.5, 1.0, 2.0, 3.0) and center-of-mass energies ( 2.36, 5.02, 7.0, 13.0 TeV). Based on the resulting distributions, we examine the KNO scaling effect and fit the distributions with the double NBD model for parameterization, aiming to accurately characterize the observed results and elucidate contributions from both soft and semi-hard processes. Our results indicate that different geometric descriptions of the oxygen nucleus project significantly different multiplicities of charged particles, especially for large multiplicities and higher pseudorapidity. We also observed that multiplicity of charged particles calculated with Pythia reveals significantly different behavior from that calculated with -factorization.
Paper Structure (8 sections, 10 equations, 7 figures)

This paper contains 8 sections, 10 equations, 7 figures.

Figures (7)

  • Figure 1: Pictorial representation of the arrangement of $\alpha$-clusters within the oxygen nuclei.
  • Figure 2: Comparison between the parameter sets for the $\alpha$-cluster and Woods-Saxon models: (a) density profile for each parameter set; (b) probability of the radial position of nucleons within the oxygen nucleus.
  • Figure 3: Charged particle multiplicity in p-O collisions. Each panel shows a different center-of-mass energy ($\sqrt{s} = 2.36, 5.02, 9.62, 13$ TeV) and compares the Alpha Cluster (AC) and Woods-Saxon (WS) models at different pseudorapidity values ($|\eta| < 0.5, 1.0, 2.0, 3.0$).
  • Figure 4: Charged particles multiplicity in p-O collisions. Comparison between the Pythia (Angantyr) and the $k_T$-factorization considering $\sqrt{s} = 13$ TeV and (a) $|\eta| < 0.5$ and (b) $|\eta| < 3.0$.
  • Figure 5: Average charged particle multiplicity (a) as a function of the center-of-mass energy and (b) as a function of pseudorapidity. Comparison between the Pythia (Angantyr) and the $k_T$-factorization considering different center-of-mass energy ($\sqrt{s} = 2.36, 5.02, 9.62, 13$ TeV) and pseudorapidity values ($|\eta| < 0.5, 1.0, 2.0, 3.0$). Figure (b) we multiply by the factor $m = 1.0, 6.0, 10.0, 20.0$ to make it easier to visualize.
  • ...and 2 more figures