A tensor decomposition model for evaluating isotopic yield in neutron-induced fission
Qufei Song, Long Zhu, Jun Su, Hui Guo

TL;DR
This paper introduces a tensor decomposition model (FYTD) for predicting isotopic yields in neutron-induced fission, demonstrating improved accuracy over existing models and capturing multi-dimensional dependencies in fission yield data.
Contribution
The FYTD model applies tensor decomposition to predict fission yields, including isotopic and mass distributions, with validation against experimental and database data, showing superior performance.
Findings
FYTD model predicts 235U and 239Pu fission yields accurately.
Predictions for isotopic yields agree within 1 order of magnitude for 98% cases.
Model captures multi-dimensional dependencies and extends to different neutron energies.
Abstract
After constructing yield tensors with three dimensions for 851 fission products and filling the tensors with the independent yield data from the ENDF/B-VIII.0 database, the tensor decomposition algorithm is applied to predict the independent isotopic yield in fission, which resulting in the Fission Yield Tensor Decomposition (FYTD) model. The fission yields of 235U and 239Pu are set as missing values and then predicted. The predictions for235U fissions by the FYTD model agree with the ENDF/B-VIII.0 data and are better than those by the Talys and BNN+Talys models. Furthermore, we predict not only the mass distribution but also the isotopic yields in the fissions. For fast neutron-induced fission of 239Pu, 98% predictions of the isotopic yields by the FYTD model agree with the ENDF/B-VIII.0 data within 1 order of magnitude. The fission yields of 238Np, 243Am, and 236Np that do not exist…
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Taxonomy
TopicsNuclear reactor physics and engineering · Nuclear Physics and Applications · Nuclear physics research studies
