Determination of the asymmetry term strength in the <i>n</i>–<i>A</i> elastic scattering process using a nonlocal optical model
Bibliographic record
Abstract
In previous works, the imaginary surface and (or) the imaginary volume depths of the optical potential were parametrized as linear functions of the projectile’s incident energy and neutron-proton asymmetry (N – Z)/A of the target nucleus. However, the obtained asymmetry strength parameters were not robust nor unique. In this work, we determine values for the strength parameters by simultaneously fitting 38 angular distribution data sets corresponding to neutron elastic scattering off chains of isotopes. For each isotopic chain, we considered the data sets that are measured at the same energy. This minimises the effect of the known energy dependence of the optical model and projects the dependence on the asymmetry term, which in turn leads to more reliable values of the strength parameters. To demonstrate the significance of the obtained strength values, we use the model to predict elastic angular distributions for neutron scattering off nuclei not considered in the χ2 analysis. Our theoretical angular distributions are in good agreement with the measured data and are also comparable to the predictions of local global models. In addition, our predicted total elastic and total reaction cross sections are in fair overall agreement with experiment. An additional result of this work is the determination of a global set of nonlocal parameters that describe neutron elastic scattering off nuclei that fall in the mass range 24 ≤ A ≤ 208 corresponding to incident neutron energies between ≈10–30 MeV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".