This note describes the details of the analysis of charged-particle pseudorapidity densities and multiplicity distributions measured by the ALICE detector in pp collisions at $\sqrt{s}$ = 0.9 and 7 TeV in specific phase space regions. The primary goal of the analysis is to provide reference measurements for Monte Carlo tuning. The pseudorapidity range |h| < 0.8 is considered and a lower $p_T$ cut is applied, at 0.15, 0.5 GeV/c and at 1 GeV/c. The choice of such phase space regions to measure the charged-particle multiplicity allows a direct comparison with the analogous results obtained by other LHC collaborations, namely ATLAS and CMS. The class of events considered are those having at least one charged particle in the kinematical ranges just described. In the note, the analysis procedure is presented, together with the corrections applied to the data, and the systematic uncertainty evaluation. The comparison of the results with different Monte Carlo generators is also shown.
The production of prompt $\rm \Lambda_{\rm c}^{+}$ baryons at midrapidity ($|y|<0.5$) was measured in central (0-10%) and mid-central (30-50%) Pb-Pb collisions at the center-of-mass energy per nucleon-nucleon pair $\sqrt{s_{\rm NN}} = 5.02$ TeV with the ALICE detector. The results are more precise, more differential in centrality, and reach much lower transverse momentum ($p_{\rm T}=1$ GeV/$c$) with respect to previous measurements performed by the ALICE, STAR, and CMS Collaborations in nucleus-nucleus collisions, allowing for an extrapolation down to $p_{\rm T}=0$. The $p_{\rm T}$-differential $\rm \Lambda_{\rm c}^{+}$/D$^0$ production ratio is enhanced with respect to the pp measurement for $4<p_{\rm T}<8$ GeV/$c$ by 3.7 standard deviations ($\sigma$), while the $p_{\rm T}$-integrated ratios are compatible within 1$\sigma$. The observed trend is similar to that observed in the strange sector for the $\Lambda/$K$^0_{\rm S}$ ratio. Model calculations including coalescence or statistical hadronization for charm-hadron formation are compared with the data.
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