0000000000204162
AUTHOR
O. Stézowski
showing 3 related works from this author
Backbending in the pear-shaped 223(90)Th nucleus: Evidence of a high-spin octupole to quadrupole shape transition in the actinides
2017
International audience; Relatively neutron-rich thorium isotopes lie at the heart of a nuclear region of nuclei exhibiting octupole correlation effects. The detailed level structure of Th223 has been investigated in measurements of γ radiation following the fusion-evaporation channel of the Pb208(O18,3n)Th223 reaction at 85 MeV beam energy. The level structure has been extended up to spin 49/2, and 33 new γ rays have been added using triple-γ coincidence data. The spins and parities of the newly observed states have been confirmed by angular distribution ratios. In addition to the two known yrast bands based on a K=5/2 configuration, a non-yrast band has been established up to spin 35/2. We…
Conceptual design of the AGATA 1$\pi$ array at GANIL
2017
The Advanced GAmma Tracking Array (AGATA) has been installed at the GANIL facility, Caen-France. This setup exploits the stable and radioactive heavy-ions beams delivered by the cyclotron accelerator complex of GANIL. Additionally, it benefits from a large palette of ancillary detectors and spectrometers to address in-beam γ-ray spectroscopy of exotic nuclei. The set-up has been designed to couple AGATA with a magnetic spectrometer, charged-particle and neutron detectors, scintillators for the detection of high-energy γ rays and other devices such as a plunger to measure nuclear lifetimes. In this paper, the design and the mechanical characteristics of the set-up are described. Based on sim…
Artificial neural networks for neutron/ γ discrimination in the neutron detectors of NEDA
2020
Three different Artificial Neural Network architectures have been applied to perform neutron/? discrimination in NEDA based on waveform and time-of-flight information. Using the coincident ?-rays from AGATA, we have been able to measure and compare on real data the performances of the Artificial Neural Networks as classifiers. While the general performances are quite similar for the data set we used, differences, in particular related to the computing times, have been highlighted. One of the Artificial Neural Network architecture has also been found more robust to time misalignment of the waveforms. Such a feature is of great interest for online processing of waveforms. Narodowe Centrum Nau…