On the impact of neutron star binaries’ natal-kick distribution on the Galactic r-process enrichment
Bibliographic record
Abstract
We study the impact of the neutron star binaries’ (NSBs) natal-kick distribution on the galactic r-process enrichment. We model the growth of a Milky Way type halo based on N-body simulation results and its star formation history based on multi-epoch abundance matching techniques. We consider that the NSBs that merge well beyond the galaxy's effective radius (>2 × Reff) do not contribute to the galactic r-process enrichment. Assuming a power-law delay-time distribution (DTD) function (∝t−1) with tmin = 30 Myr for binaries’ coalescence time-scales and an exponential profile for their natal-kick distribution with an average value of 180 km s−1, we show that up to ∼ 40 per cent of all formed NSBs do not contribute to the r-process enrichment by z = 0, either because they merge far from the galaxy at a given redshift (up to ∼ 25 per cent) or have not yet merged by today (∼ 15 per cent). Our result is largely insensitive to the details of the DTD function. Assuming a constant coalescence time-scale of 100 Myr well approximates the adopted DTD although with 30 per cent of the NSBs ending up not contributing to the r-process enrichment. Our results, although rather dependent on the adopted natal-kick distribution, represent the first step towards estimating the impact of natal kicks and DTD functions on the r-process enrichment of galaxies that would need to be incorporated in the hydrodynamical simulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".