Resonance production in p+p, p+A and A+A collisions measured with HADES

The knowledge of baryonic resonance properties and production cross sections plays an important role for the extraction and understanding of medium modifications of mesons in hot and/or dense nuclear matter. We present and discuss systematics on dielectron and strangeness production obtained with HADES on p+p, p+A and A+A collisions in the few GeV energy regime with respect to these resonances

Low mass dielectrons radiated off cold nuclear matter measured with HADES

The High Acceptance DiElectron Spectrometer HADES [1] is installed at the Helmholtzzentrum für Schwerionenforschung (GSI) accelerator facility in Darmstadt. It investigates dielectron emission and strangeness production in the 1-3 AGeV regime. A recent experiment series focusses on medium-modifications of light vector mesons in cold nuclear matter. In two runs, p+p and p+Nb reactions were investigated at 3.5 GeV beam energy; about 9·109 events have been registered. In contrast to other experiments the high acceptance of the HADES allows for a detailed analysis of electron pairs with low momenta relative to nuclear matter, where modifications of the spectral functions of vector mesons are predicted to be most prominent. Comparing these low momentum electron pairs to the reference measurement in the elementary p+p reaction, we find in fact a strong modification of the spectral distribution in the whole vector meson region

The dp-elastic cross section measurement at the deuteron kinetic energy of 2.5 GeV

New results on the differential cross section in deuteron-proton elastic scattering are obtained at the deuteron kinetic energy of 2.5 GeV with the HADES spectrometer. The angular range of 69° – 125° in the center of mass system is covered. The obtained results are compared with the relativistic multiple scattering model calculation using the CD-Bonn deuteron wave function. The data at fixed scattering angles in the c.m. are in qualitative agreement with the constituent counting rules prediction