Advanced LIGO Constraints on Neutron Star Mergers and r-process Sites
Bibliographic record
Abstract
Abstract The role of compact binary mergers as the main production site of r-process elements is investigated by combining stellar abundances of Eu observed in the Milky Way, galactic chemical evolution (GCE) simulations, and binary population synthesis models, and gravitational wave measurements from Advanced LIGO. We compiled and reviewed seven recent GCE studies to extract the frequency of neutron star–neutron star (NS–NS) mergers that is needed in order to reproduce the observed [Eu/Fe] versus [Fe/H] relationship. We used our simple chemical evolution code to explore the impact of different analytical delay-time distribution functions for NS–NS mergers. We then combined our metallicity-dependent population synthesis models with our chemical evolution code to bring their predictions, for both NS–NS mergers and black hole–neutron star mergers, into a GCE context. Finally, we convolved our results with the cosmic star formation history to provide a direct comparison with current and upcoming Advanced LIGO measurements. When assuming that NS–NS mergers are the exclusive r-process sites, and that the ejected r-process mass per merger event is 0.01 M , the number of NS–NS mergers needed in GCE studies is about 10 times larger than what is predicted by standard population synthesis models. These two distinct fields can only be consistent with each other when assuming optimistic rates, massive NS–NS merger ejecta, and low Fe yields for massive stars. For now, population synthesis models and GCE simulations are in agreement with the current upper limit (O1) established by Advanced LIGO during their first run of observations. Upcoming measurements will provide an important constraint on the actual local NS–NS merger rate, will provide valuable insights on the plausibility of the GCE requirement, and will help to define whether or not compact binary mergers can be the dominant source of r-process elements in the universe.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".