Radiative neutron capture on a proton at big-bang nucleosynthesis energies
Bibliographic record
Abstract
The total cross section for radiative neutron capture on a proton, $\mathit{np}\ensuremath{\rightarrow}d\ensuremath{\gamma}$, is evaluated at big-bang nucleosynthesis (BBN) energies. The electromagnetic transition amplitudes are calculated up to next-to-leading-order within the framework of pionless effective field theory with dibaryon fields. We also calculate the $d\ensuremath{\gamma}\ensuremath{\rightarrow}\mathit{np}$ cross section and the photon analyzing power for the $d\stackrel{\ensuremath{\rightarrow}}{\ensuremath{\gamma}}\ensuremath{\rightarrow}\mathit{np}$ process from the amplitudes. The values of low-energy constants that appear in the amplitudes are estimated by a Markov Chain Monte Carlo analysis using the relevant low-energy experimental data. Our result agrees well with those of other theoretical calculations except for the $\mathit{np}\ensuremath{\rightarrow}d\ensuremath{\gamma}$ cross section at some energies estimated by an $R$-matrix analysis. We also study the uncertainties in our estimation of the $\mathit{np}\ensuremath{\rightarrow}d\ensuremath{\gamma}$ cross section at relevant BBN energies and find that the estimated cross section is reliable to within $~1$% error.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".