Resolving the Fastest Ejecta from Binary Neutron Star Mergers: Implications for Electromagnetic Counterparts
Bibliographic record
Abstract
Abstract We examine the effect of spatial resolution on initial mass ejection in grid-based hydrodynamic simulations of binary neutron star mergers. The subset of the dynamical ejecta with velocities greater than ∼0.6c can generate an ultraviolet precursor to the kilonova on approximately hour timescales and contribute to a years long nonthermal afterglow. Previous work has found differing amounts of this fast ejecta, by one to two orders of magnitude, when using particle-based or grid-based hydrodynamic methods. Here, we carry out a numerical experiment that models the merger as an axisymmetric collision in a corotating frame, accounting for Newtonian self-gravity, inertial forces, and gravitational wave losses. The lower computational cost allows us to reach spatial resolutions as high as 4 m, or ∼3 × 10−4 of the stellar radius. We find that fast ejecta production converges to within 10% for a cell size of 20 m. This suggests that fast ejecta quantities found in existing grid-based merger simulations are unlikely to increase to the level needed to match particle-based results upon further resolution increases. The resulting neutron-powered precursors are in principle detectable out to distances ≲200 Mpc with upcoming facilities.We also find that head-on collisions at the freefall speed, relevant for eccentric mergers, yield fast and slow ejecta quantities of order 10−2 M ⊙, with a kilonova signature distinct from that of quasi-circular mergers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".