Limits on non-relativistic matter during Big-bang nucleosynthesis
Bibliographic record
Abstract
Abstract Big-bang nucleosynthesis (BBN) probes the cosmic mass-energy density at temperatures ∼ 10 MeV to ∼ 100 keV. Here, we consider the effect of a cosmic matter-like species that is non-relativistic and pressureless during BBN. Such a component must decay; doing so during BBN can alter the baryon-to-photon ratio,η, and the effective number of neutrino species. We use light element abundances and the cosmic microwave background (CMB) constraints onη andNνto place constraints on such a matter component. We find that electromagnetic decays heat the photons relative to neutrinos, and thus dilute the effective number of relativistic species toNeff< 3 for the case of three Standard Model neutrino species. Intriguingly, likelihood results based onPlanckCMB data alone findNν= 2.800 ± 0.294, and when combined with standard BBN and the observations of D and4He giveNν= 2.898 ± 0.141. While both results are consistent with the Standard Model, we find that a nonzero abundance of electromagnetically decaying matter gives a better fit to these results. Our best-fit results are for a matter species that decays entirely electromagnetically with a lifetime τX= 0.89 sec and pre-decay density that is a fractionξ= (ρX/ρrad|10 MeV = 0.0026 of the radiation energy density at 10 MeV; similarly good fits are found over a range whereξτX1/2is constant. On the other hand, decaying matter often spoils the BBN+CMB concordance, and we present limits in the (τX,ξ) plane for both electromagnetic and invisible decays. For dark (invisible) decays, standard BBN (i.e.ξ= 0) supplies the best fit. We end with a brief discussion of the impact of future measurements including CMB-S4.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".