(3+1)D hydrodynamic simulation of relativistic heavy-ion collisions
Bibliographic record
Abstract
We present music, an implementation of the Kurganov-Tadmor algorithm for relativistic 3+1 dimensional fluid dynamics in heavy-ion collision scenarios. This Riemann-solver-free, second-order, high-resolution scheme is characterized by a very small numerical viscosity and its ability to treat shocks and discontinuities very well. We also incorporate a sophisticated algorithm for the determination of the freeze-out surface using a three dimensional triangulation of the hypersurface. Implementing a recent lattice based equation of state, we compute ${p}_{T}$-spectra and pseudorapidity distributions for Au+Au collisions at $\sqrt{s}=200 \mathrm{GeV}$ and present results for the anisotropic flow coefficients ${v}_{2}$ and ${v}_{4}$ as a function of both ${p}_{T}$ and pseudorapidity $\ensuremath{\eta}$. We were able to determine ${v}_{4}$ with high numerical precision, finding that it does not strongly depend on the choice of initial condition or equation of state.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".