Issue |
EPJ Web Conf.
Volume 146, 2017
ND 2016: International Conference on Nuclear Data for Science and Technology
|
|
---|---|---|
Article Number | 04056 | |
Number of page(s) | 4 | |
Section | Fission Physics and Observables | |
DOI | https://doi.org/10.1051/epjconf/201714604056 | |
Published online | 13 September 2017 |
https://doi.org/10.1051/epjconf/201714604056
Neutron-multiplicity experiments for enhanced fission modelling
1 Division of Applied Nuclear Physics, Department of Physics and Astronomy Uppsala University, Uppsala, Sweden
2 European Commission, Joint Research Centre, Directorate G, Geel, Belgium
a e-mail: ali.al-adili@physics.uu.se
Published online: 13 September 2017
The nuclear de-excitation process of fission fragments (FF) provides fundamental information for the understanding of nuclear fission and nuclear structure in neutron-rich isotopes. The variation of the prompt-neutron multiplicity, ν(A), as a function of the incident neutron energy (En) is one of many open questions. It leads to significantly different treatments in various fission models and implies that experimental data are analyzed based on contradicting assumptions. One critical question is whether the additional excitation energy (Eexc) is manifested through an increase of ν(A) for all fragments or for the heavy ones only. A systematic investigation of ν(A) as a function of En has been initiated. Correlations between prompt-fission neutrons and fission fragments are obtained by using liquid scintillators in conjunction with a Frisch-grid ionization chamber. The proof-of-principle has been achieved on the reaction 235U(nth,f) at the Van De Graff (VdG) accelerator of the JRC-Geel using a fully digital data acquisition system. Neutrons from 252Cf(sf) were measured separately to quantify the neutron-scattering component due to surrounding shielding material and to determine the intrinsic detector efficiency. Prelimenary results on ν(A) and spectrum in correlation with FF properties are presented.
© The Authors, published by EDP Sciences, 2017
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