Measurement of flow harmonics with multi-particle cumulants in Pb+Pb collisions at $$\sqrt{s_{\mathrm {NN}}}=2.76$$ s NN = 2.76 TeV with the ATLAS detector
Bibliographic record
Abstract
ATLAS measurements of the azimuthal anisotropy in lead–lead collisions at $$\sqrt{s_{\mathrm {NN}}}=2.76$$ TeV are shown using a dataset of approximately 7 $$\upmu $$ b $$^{-1}$$ collected at the LHC in 2010. The measurements are performed for charged particles with transverse momenta $$0.5<p_{\mathrm {T}}<20$$ GeV and in the pseudorapidity range $$|\eta |<2.5$$ . The anisotropy is characterized by the Fourier coefficients, $$\mathrm {v}_n$$ , of the charged-particle azimuthal angle distribution for $$n = 2$$ –4. The Fourier coefficients are evaluated using multi-particle cumulants calculated with the generating function method. Results on the transverse momentum, pseudorapidity and centrality dependence of the $$\mathrm {v}_n$$ coefficients are presented. The elliptic flow, $$\mathrm {v}_2$$ , is obtained from the two-, four-, six- and eight-particle cumulants while higher-order coefficients, $$\mathrm {v}_3$$ and $$\mathrm {v}_4$$ , are determined with two- and four-particle cumulants. Flow harmonics $$\mathrm {v}_n$$ measured with four-particle cumulants are significantly reduced compared to the measurement involving two-particle cumulants. A comparison to $$\mathrm {v}_n$$ measurements obtained using different analysis methods and previously reported by the LHC experiments is also shown. Results of measurements of flow fluctuations evaluated with multi-particle cumulants are shown as a function of transverse momentum and the collision centrality. Models of the initial spatial geometry and its fluctuations fail to describe the flow fluctuations 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".