Addressing the Ultra-Central Puzzle with Initial-State Nuclear Structures
Bibliographic record
Abstract
Abstract Hydrodynamic models fail to describe the near-equal v 3 / v 2 ratio observed in ultra-central heavy-ion collisions, despite their success in other centrality classes. This failure can not be resolved by adjusting the shear viscous coefficient, as shear viscosity suppresses higher-order anisotropic flows more strongly, leading to an underestimation of v 3 when v 2 matches experimental data. To address this issue, we explore two initial-state modifications to resolve this puzzle: (1) impose a minimum distance between nucleons to simulate the homogenization effect arising from short-range nucleon–nucleon repulsion; and (2) introduce sub-nucleonic structures, specifically “hot spots” within protons, to provide a more refined description of initial-state fluctuations. Using TRENTo initial conditions and 3+1D viscous hydrodynamic model CLVisc, both approaches significantly lower geometric eccentricity, reduce required viscosity, and narrow the v 2 - v 3 gap in ultra-central collisions. Our results implicate initial-state nuclear and sub-nucleon structures as critical factors in addressing this puzzle. Resolving it would advance nuclear structure studies and improve precision in extracting quark–gluon plasma (QGP) transport coefficients (e.g., shear viscosity), bridging microscopic nuclear features to macroscopic QGP properties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".