- Out-of-plane pion emission in relativistic heavy ion collisions: Spectroscopy of Delta resonance matter (1993)
- Azimuthal correlations of pions are studied with the quantum molecular dynamics model. Pions are preferentially emitted perpendicular to the reaction plane. Our analysis shows that this anisotropy is dominated by pion absorption on the spectator matter in the reaction plane. Pions emitted perpendicular to the reaction plane undergo less rescattering than those emitted in the reaction plane and might therefore be more sensitive to the early hot and dense reaction phase.
- pi-N correlations probe the nuclear equation of state in relativistic heavy ion-collisions (1995)
- We investigate the sensivity of pionic bounce-off and squeeze-out on the density and momentum dependence of the real part of the nucleon optical potential. For the in-plane pion bounce-off we find a strong sensivity on both the density and momentum dependence whereas the out-of-plane pion squeeze-out shows a strong sensivity only towards the momentum dependence but little sensivity towards the density dependence.
- High pT pions as probes of the dense phase of relativistic heavy ion collisions (1994)
- The properties of pions from the hot and dense reaction stage of relativistic heavy ion collisions are investigated with the quantum molecular dynamics model. Pions originating from this reaction stage stem from resonance decay with enhanced mass. They carry high transverse momenta. The calculation shows a direct correlation between high pt pions, early freeze-out times and high freeze-out densities.
- Neural networks for impact parameter determination (1993)
- Accurate impact parameter determination in a heavy-ion collision is crucial for almost all further analysis. We investigate the capabilities of an artificial neural network in that respect. First results show that the neural network is capable of improving the accuracy of the impact parameter determination based on observables such as the flow angle, the average directed inplane transverse momentum and the difference between transverse and longitudinal momenta. However, further investigations are necessary to discover the full potential of the neural network approach.