Analysis of Multi-Particle Production at SPS and RHIC by Two-Source Statistical Model
Bibliographic record
Abstract
The data of multi-particle production in 158 A GeV Pb+Pb collisions and (?)s =130 A GeV Au+Au collisions are analyzed by two-source statistical model. It is found that in 158 A GeV Pb+Pb collisions the source is composed of a hot, small inner part and a large, cool outer part. The outer part characterizes the projectile-like and target-like, and the inner part characterizes the central reaction zone. In (?)s =130 A GeV Au+Au collisions, there is a much hotter and larger inner source. The temperature is at least 15MeV higher than that in the inner source in 158 A GeV Pb+Pb collisions. The volume is at least two times of that in the later collision. The reason is that the (?)s =130 A GeV Au+Au collision has large rapidity region [-4.9,4.9], while the experimental data are taken from a small pseudo-rapidity region, |η|0.5 in which the particles are uniformly distributed as a single source. For a source formed with uniformly-distributed particles, both the single-source statistical model and the two-source statistical model are available, while for a source formed with non-uniformly-distributed particles, only the two-source statistical model is available. The RHIC can provide a hot and large inner source that may be formed in the early stage of hadronization from QGP and may have important physical content behind. We suggest to make synchronous measurement in the outer region, e.g. 3|y|4, so as to make comparison.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".