Exploring the Thermodynamics of the Quark-Gluon Plasma Freeze-out Hypersurface
Bibliographic record
Abstract
In this thesis, we present a thermal model for Quark-Gluon Plasma (QGP) freeze-out that includes an effect which mixes contributions from freeze-out points with different rapidities in observed the final particle rapidity distribution, referred to as the smearing effect.Using this model, we obtained the thermodynamic profile of the QGP freeze-out surface by fitting particle yields from a hydrodynamic simulation.By comparing it with the standard thermal model and the hydrodynamic simulation, our study reveals significant uncertainties in both thermal models when they are applied to large rapidity regions, while for mid-rapidity they both agree well with the hydrodynamic simulation.By applying it directly to experimental data, we also demonstrate the effectiveness of the smearing thermal model in constraining particle yields and thermodynamics around mid-rapidity.However, the model gives a lower temperature than the ones obtained from using thermal models on yields from hydrodynamic simulations, highlighting the need to consider feed-down effects in future studies.i 5 Thermal model meets experimental data: a Bayesian study on BRAHMS Au+Au collisions at s = 62.4 GeV 5.1 Bayesian parameter estimation . . . . . . . . .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".