Hypermassive Neutron Star Disk Outflows and Blue Kilonovae
Bibliographic record
Abstract
Abstract We study mass ejection from accretion disks around newly formed hypermassive neutron stars (HMNSs). Standard kilonova model fits to GW170817 require at least a lanthanide-poor (“blue”) and lanthanide-rich (“red”) component. The existence of a blue component has been used as evidence for an HMNS remnant of finite lifetime, but average disk outflow velocities from existing long-term HMNS simulations fall short of the inferred value (∼0.25c) by a factor of ∼2. Here we use time-dependent, axisymmetric hydrodynamic simulations of HMNS disks to explore the limits of the model and its ability to account for observations. For physically plausible parameter choices compatible with GW170817, we find that hydrodynamic models that use shear viscosity to transport angular momentum cannot eject matter with mass-averaged velocities larger than ∼0.15c. While outflow velocities in our simulations can exceed the asymptotic value for a steady-state neutrino-driven wind, the increase in the average velocity due to viscosity is not sufficient. Therefore, viscous HMNS disk winds cannot reproduce by themselves the ejecta properties inferred from multi-component fits to kilonova light curves from GW170817. Three possible resolutions remain feasible within standard merger ejecta channels: more sophisticated radiative transfer models that allow for photon reprocessing between ejecta components, inclusion of magnetic stresses, or enhancement of the dynamical ejecta. We provide fits to our disk outflow models once they reach homologous expansion.
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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".