Pre-equilibrium evolution effects on heavy-ion collision observables
Bibliographic record
Abstract
In order to investigate the importance of pre-equilibrium dynamics on relativistic heavy-ion collision observables, we match a highly non-equilibrium early evolution stage, modeled by free-streaming partons generated from the Monte Carlo Kharzeev-Levin-Nardi (MC-KLN) and Monte Carlo Glauber (MC-Glb) models, to a locally approximately thermalized later evolution stage described by viscous hydrodynamics, and study the dependence of final hadronic transverse momentum distributions, in particular their underlying radial and anisotropic flows, on the switching time between these stages. Performing a 3-parameter fit of the measured values for the average transverse momenta $\langle p_\perp \rangle$ for pions, kaons and protons as well as the elliptic and triangular flows of charged hadrons $v_{2,3}^\mathrm{ch}$, with the switching time $\tau_s$, the specific shear viscosity $\eta/s$ during the hydrodynamic stage, and the kinetic decoupling temperature $T_\mathrm{dec}$ as free parameters, we find that the preferred times $\tau_s$ depend strongly on the model of the initial conditions. MC-KLN initial conditions require an earlier transition to hydrodynamic behavior (at $\tau_s \approx$ 0.13 fm/$c$) , followed by hydrodynamic evolution with a larger specific shear viscosity $\eta/s\approx$ 0.2, than MC-Glb initial conditions which prefer switching at a later time ($\tau_s\approx$ 0.6 fm/$c$) followed by a less viscous hydrodynamic evolution with $\eta/s\approx$ 0.16. These new results including pre-equilibrium evolution are compared to fits without a pre-equilbrium stage where all dynamic evolution before the onset of hydrodynamic behavior is ignored. In each case, the quality of the dynamical descriptions for the optimized parameter sets, as well as the observables which show the strongest constraining power for the thermalization time, are discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".