Refine
Document Type
- Article (2)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- heavy ion collisions (2) (remove)
Institute
- Frankfurt Institute for Advanced Studies (FIAS) (2) (remove)
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.
This a review of the present status of heavy-ion collisions at intermediate energies. The main goal of heavy-ion physics in this energy regime is to shed some light on the nuclear equation of state (EOS), hence we present the basic concept of the EOS in nuclear matter as well as of nuclear shock waves which provide the key mechanism for the compression of nuclear matter. The main part of this article is devoted to the models currently used for describing heavy-ion reactions theoretically and to the observables useful for extracting information about the EOS from experiments. A detailed discussion of the flow effects with a broad comparison with the avaible data is presented. The many-body aspects of such reactions are investigated via the multifragmentation break up of excited nuclear systems and a comparison of model calculations with the most recent multifragmentation experiments is presented.