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The GEneral description of Fission observables (GEF) model was developed to produce fission related nuclear data which are of crucial importance for basic and applied nuclear physics. The investigation of the performance of the GEF code is here extended to a region in fissioning-system mass, charge, excitation energy and angular momentum, as well as to new observables, that could not be benchmarked in detail so far. The work focuses on fragment mass and isotopic distributions, benefiting from recent innovative measurements. The approach reveals a high degree of consistency and provides a very reasonable description of the new data. The physics behind specific discrepancies is discussed, and hints to improve on are given. Comparison of the calculation with experiment permits to highlight the influence of the system intrinsic properties, their interplay, and the importance of experimental aspects, namely instrumental resolution. All together points to the necessity of as selective and accurate as possible experimental data, for proper unfolding of the different influences and robust interpretation of the measurement. The GEF code has become a widely used tool for this purpose
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 fragmen
The understanding of the antineutrino production in fission and the theoretical calculation of their energy spectra in different types of fission reactors rely on the application of the summation method, where the individual contributions from the di
A direct and complete measurement of isotopic fission-fragment yields of $^{239}$U has been performed for the first time. The $^{239}$U fissioning system was produced with an average excitation energy of 8.3 MeV in one-neutron transfer reactions betw
$textbf{Background}$ More than half of all the elements heavier than iron are made by the rapid neutron capture process (or r process). For very neutron-rich astrophysical conditions, such at those found in the tidal ejecta of neutron stars, nuclear
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