RESONANT ENHANCEMENT OF NUCLEAR REACTIONS AS A POSSIBLE SOLUTION TO THE COSMOLOGICAL LITHIUM PROBLEM
Bibliographic record
Abstract
There is a significant discrepancy between the current theoretical prediction of the cosmological lithium abundance, mostly produced as 7 Be during the Big Bang, and its observationally inferred value. We investigate whether the resonant enhancement of 7 Be burning reactions may alleviate this discrepancy. We identify one narrow nuclear level in 9 B , E5/2+ ≃ 16.7 MeV that is not sufficiently studied experimentally, and being just ~ 200 keV above the 7 Be +d threshold, may lead to the resonant enhancement of 7 Be (d, γ)9 B and 7 Be (d, p)αα reactions. We determine the relationship between the domain of resonant energies Er and the deuterium separation width Γd that results in the significant depletion of the cosmological lithium abundance and find that (Er, Γd)≃(170-220, 10-40) keV can eliminate the current discrepancy. Such a large width at this resonant energy can be only achieved if the interaction radius for the deuterium entrance channel is very large, a27 ≥ 10 fm . New nuclear experimental and theoretical work is needed to clarify the role this resonance plays on the BBN prediction of the lithium abundance. Alternatively, the most liberal interpretation of the allowed parameters of 16.7 MeV resonance can significantly increase the errors in predicted lithium abundance: [7 Li/H ] BBN = (2.5-6) × 10-10.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".