R. Ogul, A. S. Botvina, M. Bleicher, N. Buyukcizmeci, A. Ergun, H. Imal
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Isospin Correlations in Isotope Yields at Intermediate and High Energy Heavy-Ion Collisions
Isotopic yields of fragments emitted from projectile sources in peripheral collisions at Fermi and relativistic energy regimes are studied in the framework of the statistical multifragmentation model for an ensemble of excited both projectile and midrapidity sources. The data of FAZIA and FRS experiments and results of their theoretical analyses are compared together with each other to investigate the differences and similarities in both energy regimes. The importance of possible applications of the present results for the production of new exotic nuclei including hypernuclei in relativistic peripheral collisions that may have a broad yield distributions extending beyond the proton and neutron driplines, is emphasized.
期刊介绍:
The journal Few-Body Systems presents original research work – experimental, theoretical and computational – investigating the behavior of any classical or quantum system consisting of a small number of well-defined constituent structures. The focus is on the research methods, properties, and results characteristic of few-body systems. Examples of few-body systems range from few-quark states, light nuclear and hadronic systems; few-electron atomic systems and small molecules; and specific systems in condensed matter and surface physics (such as quantum dots and highly correlated trapped systems), up to and including large-scale celestial structures.
Systems for which an equivalent one-body description is available or can be designed, and large systems for which specific many-body methods are needed are outside the scope of the journal.
The journal is devoted to the publication of all aspects of few-body systems research and applications. While concentrating on few-body systems well-suited to rigorous solutions, the journal also encourages interdisciplinary contributions that foster common approaches and insights, introduce and benchmark the use of novel tools (e.g. machine learning) and develop relevant applications (e.g. few-body aspects in quantum technologies).