Pseudorapidity dependence of long-range correlations - with ALICE in Pb-Pb, Xe-Xe, and p-Pb collisions
Bibliographic record
Abstract
At the LHC, the strongly interacting Quark-Gluon Plasma is created in heavy-ion collisions. The collisions can be studied at ALICE. One measurable of the created plasma is anisotropic flow. The anisotropic flow measurements are closely related to the initial conditions of the collision, and to the expansion of the plasma. This work presents flow measurements as a function of pseudorapidity with data from the Time-Projection Chamber (TPC) and the Forward Multiplicity Detector (FMD). The Generic Framework is used to calculate the flow coefficients. The non-uniform acceptance and non-uniform efficiency of the detectors can be corrected by using particle weights in the Generic Framework. The Forward Multiplicity Detector (FMD) at ALICE, needs further corrections since it receives background signal from particles created from material interactions. The flow coefficients, $v_n$, are presented over a large range in pseudorapidity ($-3.5 \leq \eta \leq 5$) in Pb$-$Pb collisions with $\sqrt{s}_\mathrm{NN}=5.02$ TeV, Xe$-$Xe with $\sqrt{s}_\mathrm{NN}=5.44$ TeV, and early results for p$-$Pb with $\sqrt{s}_\mathrm{NN}=5.02$ TeV. The flow coefficients are measured with 2- and 4-particle cumulants with $|\Delta \eta | > 0.8$ and $|\Delta \eta | > 2$. The difference between $v_2\{2,|\Delta \eta|>0.8\}$ and $v_2\{2,|\Delta \eta|>2\}$ implies changes in the decorrelation of the flow vectors or non-flow effects between the two methods. The decorrelation effect of the flow vectors is studied with a decorrelation ratio $r_{n,n}$, which is presented for Pb$-$Pb and p$-$Pb collisions with $\sqrt{s}_\mathrm{NN}=5.02$ TeV. The decorrelation of the flow vectors are more significant for p$-$Pb than for Pb$-$Pb. The AMPT model is compared to the flow measurements in pseudorapidity, and the comparisons show a qualitative agreement with the measurements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".