Expansion dynamics of Pb-Pb collisions at 40 A GeV/c viewed by negatively charged hadrons

In this paper we present results on transverse mass spectra and Hanbury-Brown and Twiss correlation functions of negatively charged hadrons, which are expected to be mostly negative pions, measured in Pb-Pb collisions at 40 A GeV/c beam momentum. Based on these data, the collision dynamics and the space-time extent of the system at the thermal freeze-out are studied over a centrality range corresponding to the most central 53% of the Pb--Pb inelastic cross section. Comparisons with freeze-out conditions of strange particles and HBT results from other experiments are discussed.Comment: 29 pages, 18 figure

Charged-particle multiplicity measurement in proton–proton collisions at sqrt(s)=0.9 and 2.36 TeV with ALICE at LHC

Charged-particle production was studied in proton–proton collisions collected at the LHC with the ALICE detector at centre-of-mass energies 0.9 TeV and 2.36 TeV in the pseudorapidity range |η| < 1.4. In the central region (|η| < 0.5), at 0.9 TeV, we measure charged-particle pseudorapidity density dNch/dη = 3.02 ± 0.01(stat.) +0.08 −0.05(syst.) for inelastic interactions, and dNch/dη = 3.58 ± 0.01 (stat.) +0.12 −0.12(syst.) for non-single-diffractive interactions. At 2.36 TeV, we find dNch/dη = 3.77 ± 0.01(stat.) +0.25 −0.12(syst.) for inelastic, and dNch/dη = 4.43 ± 0.01(stat.) +0.17 −0.12(syst.) for non-single-diffractive collisions. The relative increase in charged-particle multiplicity from the lower to higher energy is 24.7% ± 0.5%(stat.) +5.7 −2.8%(syst.) for inelastic and 23.7% ± 0.5%(stat.) +4.6 −1.1%(syst.) for non-single-diffractive interactions. This increase is consistent with that reported by the CMS collaboration for non-single-diffractive events and larger than that found by a number of commonly used models. The multiplicity distribution was measured in different pseudorapidity intervals and studied in terms of KNO variables at both energies. The results are compared to proton– antiproton data and to model predictions