Collective Structure of Neutron-Rich Rare-Earth Nuclei and Development of Instrumentation for Gamma-Ray Spectroscopy

University dissertation from Uppsala : Acta Universitatis Upsaliensis

Abstract: Neutron-rich rare-earth nuclei are among the most collective nuclei that can be found in nature. In particular, the doubly mid-shell nucleus 170Dy is expected to be the nucleus where the collective structure is maximized. This has implications for the astrophysical r-process, since it has been suggested that the collectivity maximum plays an important role in the abundances of the rare-earth elements that are created in supernova explosions. In this work, the collective structure of the five nuclei 168,170Dy and 167,168,169Ho are studied and different theoretical models are used to interpret the evolution of collectivity around the mid-shell. In order to produce and study even more neutron-rich nuclei in this mass region, new radioactive ion beam facilities will be a valuable tool. These facilities, however, require advanced instruments to study the weak signals of exotic nuclei in a high background environment. Two of these instruments are the ?-ray tracking spectrometer AGATA and the neutron detector array NEDA. For AGATA to work satisfactorily, the interaction position of the gamma rays must be determined with an accuracy of at least five millimetres. The position resolution is measured in this work using a model independent method based on the Doppler correction capabilities of the detector at two different distances between the detector and the source. For NEDA, one of the critical parameters is its ability to discriminate between neutrons and ? rays. By using digital electronics it is possible to employ advanced and efficient algorithms for pulse-shape discrimination. In this work, digital versions of the common analogue methods are shownto give as good, or better, results compared to the ones obtained using analogue electronics. Another method which effectively distinguishes between neutrons and ? rays is based on artificial neural networks. This method is also investigated in this work and is shown to yield even better results.

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