Rapidity scan with multistage hydrodynamic and statistical thermal models
Bibliographic record
Abstract
We calibrate a ($3+1$)-dimensional multistage hybrid framework using the measured pseudorapidity distribution of charged particles and rapidity distribution of net protons for central Au $+$ Au collisions at $\sqrt{{s}_{NN}}=7.7,\phantom{\rule{0.16em}{0ex}}19.6,\phantom{\rule{0.16em}{0ex}}62.4,\phantom{\rule{0.16em}{0ex}}200$ GeV. We then study the thermodynamic properties of the nuclear matter along the beam direction, and the phase diagram regions probed by the hadronization process near the chemical freeze-out. Using the rapidity-dependent thermal yields of identified particles with full rapidity coverage from the hybrid framework, we apply different scenarios of the statistical thermal model to extract the thermodynamic parameters at the freeze-out, with the known system properties from the hybrid model as a closure test. We find significant theoretical uncertainties in the thermal models when applied to regions away from midrapidity. We also propose a thermal model inspired by the hybrid approach that includes thermal smearing and longitudinal flow for the nuclear matter created at low beam energies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".