Effect of pre-equilibrium spin distribution on neutron induced <sup>150</sup>Sm cross sections
Bibliographic record
Abstract
Prompt γ-ray production cross section measurements were made as a function of incident neutron energy (E n = 1 to 35 MeV) on an enriched (95.6%) 150 Sm sample.Energetic neutrons were delivered by the Los Alamos National Laboratory spallation neutron source located at the Los Alamos Neutron Science Center (LANSCE) facility.The prompt-reaction γ rays were detected with the large-scale Compton-suppressed Germanium Array for Neutron Induced Excitations (GEANIE).Above E n ≈ 8 MeV the pre-equilibrium reaction process dominates the inelastic reaction.The spin distribution transferred in pre-equilibrium neutron-induced reactions was calculated using the quantum mechanical theory of Feshbach, Kerman, and Koonin (FKK).These pre-equilibrium spin distributions were incorporated into the Hauser-Feshbach statistical reaction code GNASH and the γ-ray production cross sections were calculated and compared with experimental data.Neutron inelastic scattering populates 150 Sm excited states either by (1) forming the compound nucleus 151 Sm * and decaying by neutron emission, or (2) by the incoming neutron transferring energy to create a particle-hole pair, and thus initiating the pre-equilibrium process.These two processes produce rather different spin distributions: the momentum transfer via the pre-equilibrium process tends to be smaller than in the compound reaction.This difference in the spin population has a significant impact on the γ-ray de-excitation cascade and therefore in the partial γ-ray cross sections.The difference in the calculated partial γ-ray cross sections using spin distributions with and without pre-equilibrium effects was significant, e.g., for the 558-keV transition between 8 + and 6 + states the calculated partial γ-ray production cross sections changed by 70% at E n = 20 MeV with inclusion of the spin distribution of pre-equilibrium process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".