Nuclear spectroscopy in the chaotic domain: level densities
Bibliographic record
Abstract
In heavy nuclei, the structure generating the slow-neutron resonance spectrum extends downward in energy to ~(1–2) MeV excitation and, of course, upward as well until particle emission becomes significant, thereby generating an Embedded Gaussian Orthogonal Ensemble (EGOE) spectrum built on a secular mean-density function. In this extended chaotic domain, principles and methods for the calculation of one-point quantities (e.g., level densities, spin-cutoff factors, occupancies, etc.,) have been well developed during the last several years. The economy and the resultant generic forms follow from the dominance of unitary symmetries, central limit theorems, and quantum chaos. In this paper, techniques used for level densities are illustrated by a detailed study of several heavy nuclei, the input data being taken from the observed low-lying spectrum and the far-separated neutron-resonance spectrum, this in itself saying much about long-range spectral rigidity. Explicit forms for the interacting particle state densities, expectation values, and expectation-value densities of operators in Hamiltonian eigenstates are given. Extension of the formalism to two-point functions that deal with spectral fluctuations, transition strengths, and analysis of measures for broken symmetries and which involve the same formal structure is indicated; higher order correlation functions are of little immediate interest because they define quantities only rarely measurable.PACS Nos.: 21.10Ma, 21.60Cs, 24.60–k, 24.60.Lz
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".