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The structure effects of the fission fragments on their yields are studied within the statical theory with the inputs, like, excitation energies and level density parameters for the fission fragments at a given temperature calculated using the temperature dependent relativistic mean field formalism (TRMF). For the comparison, the results are also obtained using the finite range droplet model. At temperatures $T =1-2$ MeV, the structural effects of the fission fragments influence their yields. It is also seen that at $T = $ 3 MeV, the fragments become spherical and the fragments distribution peaks at a close shell or near close shell nucleus.
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The quasi-fission mechanism hinders fusion of heavy systems because of a mass flow between the reactants, leading to a re-separation of more symmetric fragments in the exit channel. A good understanding of the competition between fusion and quasi-fission mechanisms is expected to be of great help to optimize the formation and study of heavy and superheavy nuclei. Quantum microscopic models, such as the time-dependent Hartree-Fock approach, allow for a treatment of all degrees of freedom associated to the dynamics of each nucleon. This provides a description of the complex reaction mechanisms, such as quasi-fission, with no parameter adjusted on reaction mechanisms. In particular, the role of the deformation and orientation of a heavy target, as well as the entrance channel magicity and isospin are investigated with theoretical and experimental approaches.
In the present paper, we explore the idea of isospin conservation in new situations and contexts based on the directions provided by our earlier works. We present the results of our calculations for the relative yields of neutron-rich fission fragments emitted in fast neutron-induced fission, 238U (n, fission) reaction by using the concept of the conservation of isospin and compare with the experimental data. Our results successfully reproduced the gross features of partition wise fission fragments distribution of 238U (n, fission). This confirms that in all kinds of fission, isospin remains pure in neutron-rich systems even at high excitations. Thus, isospin can be proven as an important quantum number for the prediction of fission fragment distribution.
Probabilistic machine learning techniques can learn both complex relations between input features and output quantities of interest as well as take into account stochasticity or uncertainty within a data set. In this initial work, we explore the use of one such probabilistic network, the Mixture Density Network (MDN), to reproduce fission yields and their uncertainties. We study mass yields for the spontaneous fission of $^{252}$Cf, exploring the number of training samples needed for converged predictions, how different levels of uncertainty propagate from the training set to the MDN predictions, and how well physical constraints of the yields - such as normalization and symmetry - are upheld by the algorithm. Finally, we test the ability of the MDN to interpolate between and extrapolate beyond samples in the training set using energy-dependent mass yields for the neutron-induced fission on $^{235}$U. The MDN provides a reliable way to include and predict uncertainties and is a promising path forward for supplementing sparse sets of nuclear data.
The amount of emitted prompt neutrons from the fission fragments increases as a function of excitation energy. Yet it is not fully understood whether the increase in u(A) as a function of E_{n} is mass dependent. The share of excitation energies among the fragments is still under debate, but there are reasons to believe that the excess in neutron emission originates only from the heavy fragments, leaving u_{light}(A) almost unchanged. In this work we investigated the consequences of a mass-dependent increase in u(A) on the final mass and energy distributions. The assumptions on u(A) are essential when analysing measurements based on the 2E-technique. This choice showed to be significant on the measured observables. For example, the post-neutron emission mass yield distribution revealed changes up to 10-30%. The outcome of this work pinpoint the urgent need to determine u(A) experimentally, and in particular, how u(A) changes as a function of incident-neutron energy. Until then, many fission yields in the data libraries could be largely affected, since they were analysed based on another assumption on the neutron emission.
Recent experiments [Phys. Rev. Lett. 123, 092503(2019); Phys. Rev. Lett. 118, 222501 (2017)] have made remarkable progress in measurements of the isotopic fission-fragment yields of the compound nucleus $^{239}$U, which is of great interests for fast-neutron reactors and for benchmarks of fission models. We apply the Bayesian neural network (BNN) approach to learn existing evaluated charge yields and infer the incomplete charge yields of $^{239}$U. We found the two-layer BNN is improved compared to the single-layer BNN for the overall performance. Our results support the normal charge yields of $^{239}$U around Sn and Mo isotopes. The role of odd-even effects in charge yields has also been studied.
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