The Neutron Mean Life and Big Bang Nucleosynthesis
Bibliographic record
Abstract
We explore the effect of neutron lifetime and its uncertainty on standard big-bang nucleosynthesis (BBN). BBN describes the cosmic production of the light nuclides $^1{\rm H}$, ${\rm D}$, $^3{\rm H}$+$^3{\rm He}$, $^4{\rm He}$, and $^7{\rm Li}$+$^7{\rm Be}$ in the first minutes of cosmic time. The neutron mean life $τ_n$ has two roles in modern BBN calculations: (1) it normalizes the matrix element for weak $n \leftrightarrow p$ interconversions, and (2) it sets the rate of free neutron decay after the weak interactions freeze out. We review the history of the interplay between $τ_n$ measurements and BBN, and present a study of the sensitivity of the light element abundances to the modern neutron lifetime measurements. We find that $τ_n$ uncertainties dominate the predicted $^4{\rm He}$ error budget, but these theory errors remain smaller than the uncertainties in $^4{\rm He}$ observations, even with the dispersion in recent neutron lifetime measurements. For the other light-element predictions, $τ_n$ contributes negligibly to their error budget. Turning the problem around, we combine present BBN and cosmic microwave background (CMB) determinations of the cosmic baryon density to $\textit{predict}$ a "cosmologically preferred" mean life of $τ_{n}({\rm BBN+CMB}) = 870 \pm 16 \ \rm sec$, which is consistent with experimental mean life determinations. We go on to show that if future astronomical and cosmological helium observations can reach an uncertainty of $σ_{\rm obs}(Y_p) = 0.001$ in the $^4{\rm He}$ mass fraction $Y_p$, this could begin to discriminate between the mean life determinations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".