An approach to the study of the thermally driven deconfinement phase transition in a finite volume through the order parameter, its derivatives, and cumulants of the probability distribution
Bibliographic record
Abstract
We describe the temperature driven deconfining phase transition between hadronic and quark–gluon plasma (QGP) phases coexisting in a finite volume by means of a probability distribution using a simple thermodynamic model. The equations of state of both phases are calculated, where the colour singletness requirement is considered for the QGP phase with massless up and down quarks. We emphasize in this work the probability distribution and try to deeply analyze it to extract information about the transition. Also, the mean values of some response functions, which are mainly the order parameter, its three first thermal derivatives, and the second, third, and fourth cumulants of the probability distribution, are calculated and their behavior with temperature at vanishing chemical potential and at different volumes is examined. The striking result is the large similarity noted between the behavior of the order parameter derivatives and that of their homologous cumulants of the probability density. This similarity is worked out, and particularly the linearity between the thermal susceptibility and the variance is probed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".