Paper detail

A tensor decomposition model for evaluating isotopic yield in neutron-induced fission

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 in the ENDF/B-VIII.0 database are predicted and compared with those in the JEFF-3.3 database, as well as the experimental data. Good agreement demonstrates the predictive ability of the FYTD model for the target nucleus dependence. The scalability of the yield tensor decomposition model over the incident neutron energy degrees of freedom is examined. After adding a set of 2 MeV neutron-induced 239Pu fission yield data into the yield tensor, the 2 MeV neutron-induced fission yields of 235U and 239Pu are predicted. The comparison with the experimental data shows that the predictions are similar to those by the GEF model in the peak area but more accurate in the valley area. Finally, the yields of the ratio of isomeric states and neutron excess of the products as a function of product charge number are also studied. The FYTD model can capture the multi-dimensional dependence of the fission yield data and make reasonable predictions.

preprint2022arXivOpen access
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.