A Magnetar Engine for Short GRBs and Kilonovae
Bibliographic record
Abstract
Abstract We investigate the influence of magnetic fields on the evolution of binary neutron star (BNS) merger remnants via three-dimensional (3D) dynamical-spacetime general-relativistic magnetohydrodynamic (MHD) simulations. We evolve a post-merger remnant with an initial poloidal magnetic field, resolve the magnetoturbulence driven by shear flows, and include a microphysical finite-temperature equation of state. A neutrino leakage scheme that captures the overall energetics and lepton number exchange is also included. We find that turbulence induced by the magnetorotational instability in the hypermassive neutron star (HMNS) amplifies magnetic field to beyond magnetar strength (10 15 G). The ultra-strong toroidal field is able to launch a relativistic jet from the HMNS. We also find a magnetized wind that ejects neutron-rich material with a rate of . The total ejecta mass in our simulation is 5 × 10 −3 M ⊙ . This makes the ejecta from the HMNS an important component in BNS mergers and a promising source of r -process elements that can power a kilonova. The jet from the HMNS reaches a terminal Lorentz factor of ∼5 in our highest-resolution simulation. The formation of this jet is aided by neutrino cooling preventing the accretion disk from protruding into the polar region. As neutrino pair-annihilation and radiative processes in the jet (which were not included in the simulations) will boost the Lorentz factor in the jet further, our simulations demonstrate that magnetars formed in BNS mergers are a viable engine for short gamma-ray bursts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".