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Record W2033543621 · doi:10.1139/p06-047

Nuclear spectroscopy in the chaotic domain: level densities

2006· article· en· W2033543621 on OpenAlexaffvenue
J.B. French, Shaheen Rab, J. F. Smith, Rizwan Haq, V. K. Brahman Kota

Bibliographic record

VenueCanadian Journal of Physics · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum chaos and dynamical systems
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPhysicsHamiltonian (control theory)Eigenvalues and eigenvectorsChaoticGaussianQuantum mechanicsCutoffNeutronStatistical physics

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.202
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2006
Admission routes2
Has abstractyes

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