Prediction of (n, 2n) reaction cross-sections of long-lived fission products based on tensor model

Interest has recently emerged in potential applications of (n, 2n) reactions of unstable nuclei. Challenges have arisen because of the scarcity of experimental cross-sectional data. This study aims to predict the (n, 2n) reaction cross-section of long-lived fission products based on a tensor model. This tensor model is an extension of the collaborative filtering algorithm used for nuclear data. It is based on tensor decomposition and completion to predict (n, 2n) reaction cross-sections; the corresponding EXFOR data are applied as training data. The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases. Predictions were made for long-lived fission products such as ⁶⁰Co, \({^{79}} {\mathrm{Se}}\) $_{}^{79}\mathrm{Se}$ , \({^{93}}\mathrm{Zr}\) $_{}^{93}\mathrm{Zr}$ , \({^{107}}\mathrm{P}\) $_{}^{107}\mathrm{P}$ , \({^{126}}\mathrm{Sn}\) $_{}^{126}\mathrm{Sn}$ , and \({^{137}}\mathrm{Cs}\) $_{}^{137}\mathrm{Cs}$ , which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes. This method could be a powerful tool for completing (n, 2n) reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.