Viscous hydrodynamic evolution of neutron star merger accretion disks: a code comparison
Bibliographic record
Abstract
The accretion disk formed after a neutron star merger is an important contributor to the total ejecta from the merger, and hence to the kilonova and the $r$-process yields of each event. Axisymmetric viscous hydrodynamic simulations of these disks can capture thermal mass ejection due to neutrino absorption and in the advective phase -- after neutrino cooling has subsided -- and are thus likely to provide a lower-limit to the total disk ejecta relative to MHD evolution. Here we present a comparison between two viscous hydrodynamic codes that have been used extensively on this problem over the past decade: ALCAR and FLASH. We choose a representative setup with a black hole at the center, and vary the treatment of viscosity and neutrino transport. We find good overall agreement ($\sim 10\%$ level) in most quantities. The average outflow velocity is sensitive to the treatment of the nuclear binding energy of heavy nuclei, showing a larger variation than other quantities. We post-process trajectories from both codes with the same nuclear network, and explore the effects of code differences on nucleosynthesis yields, heating rates, and kilonova light curves. For the latter, we also assess the effect of varying the number of tracer particles in reconstructing the spatial abundance distribution for kilonova light curve production.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".