Results on the transverse spherocity dependence of light-flavor particle production ($\pi$, K, p, $\phi$, ${\rm K^{*0}}$, ${\rm K}^{0}_{\rm{S}}$, $\Lambda$, $\Xi$) at midrapidity in high-multiplicity pp collisions at $\sqrt{s} = 13$ TeV were obtained with the ALICE apparatus. The transverse spherocity estimator ($S_{{\rm O}}^{{\it p}_{\rm T}=1}$) categorizes events by their azimuthal topology. Utilizing narrow selections on $S_{\text{O}}^{{\it p}_{\rm T}=1}$, it is possible to contrast particle production in collisions dominated by many soft initial interactions with that observed in collisions dominated by one or more hard scatterings. Results are reported for two multiplicity estimators covering different pseudorapidity regions. The $S_{{\rm O}}^{{\it p}_{\rm T}=1}$ estimator is found to effectively constrain the hardness of the events when the midrapidity ($\left | \eta \right |<~ 0.8$) estimator is used. The production rates of strange particles are found to be slightly higher for soft isotropic topologies, and severely suppressed in hard jet-like topologies. These effects are more pronounced for hadrons with larger mass and strangeness content, and observed when the topological selection is done within a narrow multiplicity interval. This demonstrates that an important aspect of the universal scaling of strangeness enhancement with final-state multiplicity is that high-multiplicity collisions are dominated by soft, isotropic processes. On the contrary, strangeness production in events with jet-like processes is significantly reduced. The results presented in this article are compared with several QCD-inspired Monte Carlo event generators. Models that incorporate a two-component phenomenology, either through mechanisms accounting for string density, or thermal production, are able to describe the observed strangeness enhancement as a function of $S_{{\rm O}}^{{\it p}_{\rm T}=1}$.
Accepted by: JHEP
e-Print: arXiv:2310.10236 | PDF | inSPIRE
CERN-EP-2023-215
Figure group
Figure 1
Upper panels: The measured and fully corrected \SOPT distributions. Lower panels: Ratio between model calculations and experimental data. These are presented for \tracklet 0--1\% (top), 0--10\% (middle) and V0M 0--1\% (bottom). The curves represent different model predictions, where the shaded area represents the statistical uncertainty of the models. The relative systematic uncertainty is shown as a gray area around unity in the lower panels. |    |
Figure 2
Correlation between $\langle p_{\rm{T}}\rangle$ and $\langle \rm{d}N_{\pi}/\rm{d}y \rangle$ as a function of \SOPT, in the 0--10\% and 0--1\% V0M and \tracklet multiplicity classes. The total systematic uncertainties are represented by empty boxes. The statistical uncertainty is smaller than the reported marker sizes. |  |
Figure 3
PYTHIA 8.2 correlation study between $\left<\pthat\right>$ and $\left< n>$ as a function of \SOPT, in 0--1\% V0M and \tracklet multiplicity classes. The default PYTHIA 8.2 Monash variation is compared to PYTHIA 8.2 with color rope hadronization. The total systematic and statistical uncertainties are smaller than the marker sizes. The grey band is an interpolation between the points, to more clearly illustrate the trend of each multiplicity and model variation. < /n> |  |
Figure 4
Transverse momentum distribution of \pikp, \KSTAR, \PHI, \KOs, \LA and \XI for \SOPT classes selected for events at high-multiplicity, determined by events in the top 1\% of \tracklet. The lower panels present the ratio between the \SOPT-integrated and \SOPT-differential events. Statistical and total systematic uncertainties are shown by error bars and boxes, respectively. The curves represent PYTHIA 8.2 model predictions of the same measurement. |   |
Figure 5
Transverse momentum distribution of \pikp, \KSTAR, \PHI, \KOs, \LA and \XI for \SOPT classes selected for events at high-multiplicity, determined by events in the top 1\% of \tracklet. The lower panels present the ratio between the \SOPT-integrated and \SOPT-differential events. Statistical and total systematic uncertainties are shown by error bars and boxes, respectively. Fig. \ref{Fig:CombSpectraA} and Fig. \ref{Fig:CombSpectraB} both show the same experimental data. The curves represent Herwig 7.2 and EPOS-LHC predictions of the same measurement. |   |
Figure 6
The \mpt and \myield as a function of particle masses obtained for the various particle species in \SOPT classes selected for high-multiplicity events, determined by the events in the 0--1\% of \tracklet. Upper (lower) panels show the \mpt (\myield). The total systematic uncertainty is represented by the shaded regions. The measured data is compared to predictions from PYTHIA8 Monash, PYTHIA8 Rope, EPOS-LHC, and Herwig 7.2. |     |
Figure 8
Top panels show hadron-to-\PI ratios for 0--10\% \SOPT classes selected for the 0--1\% \tracklet multiplicity events. Bottom panels present the hadron-to-\PI double ratios of \SOPT classes relative to \SOPT integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from PYTHIA 8.2 Monash and Ropes. |  |
Figure 10
Top panels show hadron-to-\PI ratios for 0--10\% \SOPT classes selected for the 0--1\% \tracklet multiplicity events. Figure \ref{Fig:CombRatioPy} and Fig. \ref{Fig:CombRatioHer} both contain the same experimental data, but the vertical ranges are modified to accommodate the model predictions. Bottom panels present the hadron-to-\PI double ratios of \SOPT classes relative to \SOPT integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from Herwig 7.2 and EPOS-LHC. |  |
Figure 12
Top panels show hadron-to-\PI ratios for 0--1\% \SOPT classes selected for the 0--1\% \tracklet multiplicity events. Bottom panels present the hadron-to-\PI double ratios of \SOPT classes relative to \SOPT integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from PYTHIA 8.2 Monash and Ropes. |  |
Figure 14
Top panels show hadron-to-\PI ratios for 0--1\% \SOPT classes selected for the 0--1\% \tracklet multiplicity events. Figure \ref{Fig:CombRatioPyEx} and Fig. \ref{Fig:CombRatioHerEx} both contain the same experimental data, but the vertical ranges are modified to accommodate the model predictions. Bottom panels present the hadron-to-\PI double ratios of \SOPT classes relative to \SOPT integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Data are compared with PYTHIA 8.2 Monash predictions, and the PYTHIA 8.2 rope hadronization framework. The large fluctuations present in the Herwig 7.2 predictions are due to statistical limitations. |  |
Figure 15
p/\PI, \LA/$K$ and \XI/\PHI ratios for different \SOPT classes are obtained for $0-1\%$ events measured by the \tracklet Lower panels show the ratio to \SOPT-integrated event selection Statistical and total systematic uncertainties are shown by bars and boxes, respectively. The curves represent different model predictions of the same measurement. |  |
Figure 16
The neutral-to-charged \kzero/K ratios as a function of different multiplicity estimators and \SOPT. Statistical and total systematic uncertainties are shown by bars and boxes, respectively. The curves represent PYTHIA 8.2 model predictions of the same measurement. |   |
Figure 17
The double ratios of integrated yields as a function of \SOPT for the spectra of top-1\% \tracklet. The yield is estimated by extrapolating the spectra over the full \pt range. Statistical and systematic uncertainties are shown by bars and boxes, respectively. The grey band around unity represents the systematic uncertainty of the pion measurement. |  |
Figure 18
The double ratios of integrated yield as a function of \SOPT are represented in the top-1\% of \tracklet. The yields are integrated in the measured \pt ranges for each particle species. Statistical and systematic uncertainties are shown by bars and boxes, respectively. The curves represent different model predictions of the same measurement. The grey band around unity represents the systematic uncertainty of the pion measurement. |  |
Figure 19
The double ratios of integrated yield as a function of \SOPT are represented in the top-1\% of \tracklet. The yields are integrated in measured \pt ranges for each particle species. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Figure \ref{Fig:CL1_1_Int_py} and Fig. \ref{Fig:CL1_1_Int_hw} both contain the same experimental data, but the vertical ranges are modified to accommodate the model predictions. The curves represent different model predictions of the same measurement. The grey band around unity represents the systematic uncertainty of the pion measurement. |  |
Figure 21
Top panels show hadron-to-\PI ratios for 0--1\% \SOPT classes selected for the 0--10\% \tracklet multiplicity events. Bottom panels present the hadron-to-\PI double-ratios of \SOPT classes relative to \SOPT integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from PYTHIA 8.2 Monash and Rope variants. |  |
Figure 23
Top panels show hadron-to-\PI ratios for 0--10\% \SOPT classes selected for the 0--1\% V0M multiplicity events. Bottom panels present the hadron-to-\PI double-ratios of \SOPT classes relative to \SOPT integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from PYTHIA 8.2 Monash and Rope variants. |  |
Figure 24
The double ratios of integrated yield as a function of \SOPT are presented for V0M 0--1\% (upper) and \tracklet 0--10\% (lower). Left and right panels show the same data points, but with different model predictions. Statistical and total systematic uncertainties are shown by bars and boxes, respectively. The curves represent different model predictions of the same measurement. |     |
