Short gamma-ray burst jet propagation in binary neutron star merger environments
Bibliographic record
Abstract
ABSTRACT The multimessenger event GW170817/GRB 170817A confirmed that binary neutron star (BNS) mergers can produce short gamma-ray burst (SGRB) jets. This evidence promoted new investigations on the mechanisms through which a BNS merger remnant can launch such a powerful relativistic outflow and on the propagation of the latter across the surrounding post-merger environment. In particular, great strides have been made in jet propagation models, establishing connections between the initial jet launching conditions, including the incipient jet launching time (with respect to merger) and the injection parameters, and the observable SGRB prompt and afterglow emission. However, present semi-analytical models and numerical simulations (with one notable exception) adopt simple handmade prescriptions to account for the post-merger environment, lacking a direct association with any specific merging BNS system. Here, we present the first three-dimensional relativistic hydrodynamics simulations of incipient SGRB jets propagating through a post-merger environment that is directly imported from the outcome of a previous general relativistic BNS merger simulation. Our results show that the evolution and final properties of the jet can be largely affected by the anisotropies and the deviations from axisymmetry and homologous expansion characterizing more realistic BNS merger environments. In addition, we find that the inclusion of the gravitational pull from the central compact object, often overlooked, can have a major impact. Finally, we consider different jet launching times referred to the same BNS merger model and discuss the consequences for the ultimate jet properties.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".