The role of magnetic field geometry in the evolution of neutron star merger accretion discs
Bibliographic record
Abstract
ABSTRACT Neutron star mergers are unique laboratories of accretion, ejection, and r-process nucleosynthesis. We used 3D general relativistic magnetohydrodynamic simulations to study the role of the post-merger magnetic geometry in the evolution of merger remnant discs around stationary Kerr black holes. Our simulations fully capture mass accretion, ejection, and jet production, owing to their exceptionally long duration exceeding 4 s. Poloidal post-merger magnetic field configurations produce jets with energies Ejet ∼ (4–30) × 1050 erg, isotropic equivalent energies Eiso ∼ (4–20) × 1052 erg, opening angles θjet ∼ 6–13°, and durations tj ≲ 1 s. Accompanying the production of jets is the ejection of $f_\mathrm{ej}\sim 30\!-\!40{{\ \rm per\ cent}}$ of the post-merger disc mass, continuing out to times >1 s. We discover that a more natural, purely toroidal post-merger magnetic field geometry generates large-scale poloidal magnetic flux of alternating polarity and striped jets. The first stripe, of $E_\mathrm{jet}\simeq 2\times 10^{48}\, \mathrm{erg}$, Eiso ∼ 1051 erg, θjet ∼ 3.5–5°, and tj ∼ 0.1 s, is followed by ≳4 s of striped jet activity with $f_\mathrm{ej}\simeq 27{{\ \rm per\ cent}}$. The dissipation of such stripes could power the short-duration gamma-ray burst (sGRB) prompt emission. Our simulated jet energies and durations span the range of sGRBs. We find that although the blue kilonova component is initially hidden from view by the red component, it expands faster, outruns the red component, and becomes visible to off-axis observers. In comparison to GW 170817/GRB 170817A, our simulations underpredict the mass of the blue relative to red component by a factor of few. Including the dynamical ejecta and neutrino absorption may reduce this tension.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".