Present Status of Intermediate Reaction Theories

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Until recently, the fields of nuclear structure and of nuclear reactions had very little overlap. The analysis and interpretation of compound nuclear reactions, in particular, were almost entirely disconnected from the rest of nuclear physics. The reason was that practically nothing was known about the usefulness of simple models for the compound nuclear states. However, the giant dipole resonance (cf [10]) shows that simple modes of excitation may exist at high excitation energy. Until about ten years ago, this phenomenon was considered as a splendid exception. Its interpretation (cf [27], [58], [120]) required practically no use of reaction theory except for some details, like for instance the interpretation of the total width of the giant dipoie resonance (cf [36]). Progress in the dynamical understanding of reaction processes (cf [47], [48], [134]) led to the suggestion (cf [75]) that simple modes of excitation may also be able to produce a characteristic energy dependence of the average partial widths in particle channels just like the dipole state produces a localized enhancement of the radiative widths. The expression “intermediate structure” was coined for this phenomenon. The discovery of isobaric analogue resonances (cf [105]) provided an ideal guinea pig for the improvement and extension of intermediate structure theory (cf [49], [78], [84], [90], [108], [127]). It showed that nuclear models can be useful at high excitation energy and that, conversely, valuable dynamical information can sometimes be obtained from resonance reactions. This is in remarkable contrast with the statistical model of nuclear reactions (cf [19]), which is essentially based on the assumption that the compound nucleus does not retain any simple dynamical feature.KeywordsGiant Dipole ResonanceStrength FunctionIntermediate StructureDipole StateHigh Excitation EnergyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.