Can Neutron-star Mergers Explain the r-process Enrichment in Globular Clusters?
Bibliographic record
Abstract
Abstract Star-to-star dispersion of r-process elements has been observed in a significant number of old, metal-poor globular clusters (GCs). We investigate early-time neutron-star mergers as the mechanism for this enrichment. Through both numerical modeling and analytical arguments, we show that neutron-star mergers cannot be induced through dynamical interactions early in the history of the cluster, even when the most liberal assumptions about neutron-star segregation are assumed. Therefore, if neutron-star mergers are the primary mechanism for r-process dispersion in GCs, they likely result from the evolution of isolated, primordial binaries in the clusters. Through population modeling of double neutron-star progenitors, we find that most enrichment candidates are fast-merging systems that undergo a phase of mass transfer involving a naked He-star donor. Only models where a significant number of double neutron-star progenitors proceed through this evolutionary phase give rise to moderate fractions of GCs with enrichment; under various assumptions for the initial properties of GCs, a neutron-star merger with the potential for enrichment will occur in ∼15%–60% (∼30%–90%) of GCs if this phase of mass transfer proceeds stably (unstably). The strong anti-correlation between the pre-supernova orbital separation and post-supernova systemic velocity due to mass loss in the supernova leads to efficient ejection of most enrichment candidates from their host clusters. Thus, most enrichment events occur shortly after the double neutron stars are born. This Requires star-forming gas that can absorb the r-process ejecta to be present in the globular cluster 30–50 Myr after the initial burst of star formation. If scenarios for redistributing gas in GCs cannot act on these timescales, the number of neutron-star merger enrichment candidates drops severely, and it is likely that another mechanism, such as r-process enrichment from collapsars, is at play.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".