Projecting the likely importance of weak-interaction-driven bulk viscosity in neutron star mergers
Bibliographic record
Abstract
ABSTRACT In this work, we estimate how much bulk viscosity driven by Urca processes is likely to affect the gravitational wave signal of a neutron star coalescence. In the late inspiral, we show that bulk viscosity affects the binding energy at fourth post-Newtonian order. Even though this effect is enhanced by the square of the gravitational compactness, the coefficient of bulk viscosity is likely too small to lead to observable effects in the waveform during the late inspiral, when only considering the orbital motion itself. In the post-merger, however, the characteristic time-scales and spatial scales are different, potentially leading to the opposite conclusion. We post-process data from a state-of-the-art equal-mass binary neutron star merger simulation to estimate the effects of bulk viscosity (which was not included in the simulation itself). In that scenario, we find that bulk viscosity can reach high values in regions of the merger. We compute several estimates of how much it might directly affect the global dynamics of the considered merger scenario, and find that it could become significant. Even larger effects could arise in different merger scenarios or in simulations that include non-linear effects. This assessment is reinforced by a quantitative comparison with relativistic heavy-ion collisions where such effects have been explored extensively.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".