An asymmetric fission island driven by shell effects in light fragments
Bibliographic record
Abstract
Abstract Nuclear fission leads to the splitting of a nucleus into two fragments 1,2 . Studying the distribution of the masses and charges of the fragments is essential for establishing the fission mechanisms and refining the theoretical models 3,4 . It has value for our understanding of r-process nucleosynthesis 5,6 , in which the fission of nuclei with extreme neutron-to-proton ratios is pivotal for determining astrophysical abundances and understanding the origin of the elements 7 and for energy applications 8,9 . Although the asymmetric distribution of fragments is well understood for actinides (elements in the periodic table with atomic numbers from 89 to 103) based on shell effects 10 , symmetric fission governs the scission process for lighter elements. However, unexpected asymmetric splits have been observed in neutron-deficient exotic nuclei 11 , prompting extensive further investigations. Here we present measurements of the charge distributions of fission fragments for 100 exotic fissioning systems, 75 of which have never been measured, and establish a connection between the neutron-deficient sub-lead region and the well-understood actinide region. These new data comprehensively map the asymmetric fission island and provide clear evidence for the role played by the deformed Z = 36 proton shell of the light fragment in the fission of sub-lead nuclei. Our dataset will help constrain the fission models used to estimate the fission properties of nuclei with extreme neutron-to-proton ratios for which experimental data are unavailable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".