Evidence from Disrupted Halo Dwarfs that r-process Enrichment via Neutron Star Mergers is Delayed by ≳500 Myr
Bibliographic record
Abstract
Abstract The astrophysical origins of r-process elements remain elusive. Neutron star mergers (NSMs) and special classes of core-collapse supernovae (rCCSNe) are leading candidates. Due to these channels’ distinct characteristic timescales (rCCSNe: prompt, NSMs: delayed), measuring r-process enrichment in galaxies of similar mass but differing star formation durations might prove informative. Two recently discovered disrupted dwarfs in the Milky Way’s stellar halo, Kraken and Gaia-Sausage Enceladus (GSE), afford precisely this opportunity: Both have M ⋆ ≈ 108 M ⊙ but differing star formation durations of ≈2 Gyr and ≈3.6 Gyr. Here we present R ≈ 50,000 Magellan/MIKE spectroscopy for 31 stars from these systems, detecting the r-process element Eu in all stars. Stars from both systems have similar [Mg/H] ≈ −1, but Kraken has a median [Eu/Mg] ≈ −0.1 while GSE has an elevated [Eu/Mg] ≈ 0.2. With simple models, we argue NSM enrichment must be delayed by 500–1000 Myr to produce this difference. rCCSNe must also contribute, especially at early epochs, otherwise stars formed during the delay period would be Eu free. In this picture, rCCSNe account for ≈50% of the Eu in Kraken, ≈25% in GSE, and ≈15% in dwarfs with extended star formation durations like Sagittarius. The inferred delay time for NSM enrichment is 10×–100× longer than merger delay times from stellar population synthesis—this is not necessarily surprising because the enrichment delay includes time taken for NSM ejecta to be incorporated into subsequent generations of stars. For example, this may be due to natal kicks that result in r-enriched material deposited far from star-forming gas, which then takes ≈108–109 yr to cool in these galaxies.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".