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Emission of light fragments at small angles is studied in relativistic heavy ion collisions using the Diogene plastic wall for both symmetrical and non-symmetrical target-projectile systems with 400 MeV per nucleon and 800 MeV per nucleon incident neon nuclei. Efficiency of multiplicity measurements in the small angle range for the selection of central or peripheral collisions is confirmed for asymmetric systems. Differential production cross sections of Z = 1 fragments show evidence for the existence of two emitting sources. The apparent temperature of each source is obtained from comparison with a thermodynamical model.
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Central collisions of O16 nuclei with the Ag107 and Br80 nuclei in nuclear emulsion at 14.6, 60, and 200 GeV/nucleon are compared with proton-emulsion data at equivalent energies. The multiplicities of produced charged secondaries are consistent with the predictions of superposition models. At 200 GeV/nucleon the central particle pseudorapidity density is 58±2 for those events with multiplicities exceeding 200 particles.
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Experimental data on multiplicities and correlations of charged particles of different types produced in collisions of 4.5 A GeV/c carbon-12 with emulsion are reported and discussed. The data are compared with the results of other experiments on nucleus–nucleus and hadron–nucleus collisions. It is found that the particle production mechanism in nucleus–nucleus collisions is almost the same as in hadron–nucleus collisions. It is also observed that the shower particles' multiplicity distributions obey a KNO type scaling law, which supports the aforementioned result.
Neutral-pion production in pp interactions has been studied using 8000 photon conversions in the Fermilab 15-ft bubble chamber. Inclusive π0 multiplicity moments and ππ correlation integrals are presented; f200 is determined to be + 3.0±0.8. For the semi-inclusive π0 multiplicity distributions we find 〈n(π0)〉n− to increase with n−, while the dispersions are n− independent. Results on f2−0, f200, and f2,n−00 are compared to predictions of simple cluster models.