Transverse momentum spectra of hadrons in $p+p$ collisions at CERN SPS energies from the UrQMD transport model

TL;DR

This study evaluates the UrQMD transport model's ability to reproduce transverse momentum spectra of various hadrons in proton-proton collisions at SPS energies, revealing significant deviations and challenges for the model.

Contribution

The paper provides a detailed comparison of UrQMD model predictions with recent experimental data across multiple energies and particle types, highlighting areas of agreement and discrepancy.

Findings

UrQMD underpredicts charged kaon and proton yields at certain energies.

Model's agreement with pion spectra decreases at lower energies.

Significant deviations indicate the need for model improvements.

Abstract

The UrQMD transport model, version 3.4, is used to study the new experimental data on transverse momentum spectra of $\pi^{\pm}$, $K^{\pm}$, $p$ and $\bar p$ produced in inelastic $p+p$ interactions at SPS energies, recently published by the NA61/SHINE Collaboration. The comparison of model predictions to these new measurements is presented as a function of collision energy for central and forward particle rapidity intervals. In addition, the inverse slope parameters characterizing the transverse momentum distributions are extracted from the predicted spectra and compared to the corresponding values obtained from NA61/SHINE distributions, as a function of particle rapidity and collision energy. A complex pattern of deviations between the experimental data and the UrQMD model emerges. For charged pions, the fair agreement visible at top SPS energies deteriorates with the decreasing energy. For charged $K$ mesons, UrQMD significantly underpredicts positive kaon production at lower beam momenta. It also underpredicts the central rapidity proton yield at top collision energy and overpredicts antiproton production at all considered energies. We conclude that the new experimental data analyzed in this paper still constitute a challenge for the present version of the model.