Indirect measurements of neutron-induced reaction cross sections at heavy-ion storage rings
Bibliographic record
Abstract
Neutron-induced reaction cross sections of unstable nuclei are essential for understanding the synthesis of heavy elements in stars and for applications in nuclear technology. However, their measurement is very complicated due to the radioactivity of the targets involved. We propose to circumvent this problem by using the surrogate reaction method in inverse kinematics, where the nucleus formed in the neutron- induced reaction of interest is produced by a reaction involving a radioactive heavy-ion beam and a stable, light target nucleus. The probabilities as a function of the compound-nucleus excitation energy for γ-ray emission, neutron emission and fission, which can be measured with the surrogate reaction, are particularly useful to constrain model parameters and to obtain more accurate predictions of the neutron-induced reaction cross sections of interest. Yet, the full development of the surrogate method is hampered by numerous long- standing target issues, which can be solved by combining surrogate reactions with the unique and largely unexplored possibilities at heavy-ion storage rings. In this contribution, we describe the developments we are carrying out to measure for the first time simultaneously γ-ray emission, neutron emission and fission probabilities at the storage rings of the GSI/FAIR facility. In particular, we will present the first results of the proof of principle experiment, which we performed in June 2022 at the Experimental Storage Ring (ESR) of GSI/FAIR.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".