The Lanthanide Fraction Distribution in Metal-poor Stars: A Test of Neutron Star Mergers as the Dominant r-process Site
Bibliographic record
Abstract
Abstract Multimessenger observations of the neutron star merger GW170817 and its kilonova proved that neutron star mergers can synthesize large quantities of r-process elements. If neutron star mergers in fact dominate all r-process element production, then the distribution of kilonova ejecta compositions should match the distribution of r-process abundance patterns observed in stars. The lanthanide fraction (X La) is a measurable quantity in both kilonovae and metal-poor stars, but it has not previously been explicitly calculated for stars. Here we compute the lanthanide fraction distribution of metal-poor stars ([Fe/H] < − 2.5) to enable comparison to current and future kilonovae. The full distribution peaks at log X La ∼ −1.8, but r-process-enhanced stars ([Eu/Fe] > 0.7) have distinctly higher lanthanide fractions: . We review observations of GW170817 and find general consensus that the total , somewhat lower than the typical metal-poor star and inconsistent with the most highly r-enhanced stars. For neutron star mergers to remain viable as the dominant r-process site, future kilonova observations should be preferentially lanthanide-rich (including a population of ∼10% with ). These high-X La kilonovae may be fainter and more rapidly evolving than GW170817, posing a challenge for discovery and follow-up observations. Both optical and (mid-)infrared observations will be required to robustly constrain kilonova lanthanide fractions. If such high-X La kilonovae are not found in the next few years, that likely implies that the stars with the highest r-process enhancements have a different origin for their r-process elements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".