Measurement of long-range multiparticle azimuthal correlations with the subevent cumulant method in pp and p+Pb collisions with the ATLAS detector at the CERN Large Hadron Collider
Bibliographic record
Abstract
A detailed study of multiparticle azimuthal correlations is presented using pp data at √s = 5.02 and 13 TeV, √ and p+Pb data at √sNN = 5.02 TeV, recorded with the ATLAS detector at the CERN Large Hadron Collider. The azimuthal correlations are probed using four-particle cumulants cₙ {4} and flow coefficients vₙ {4} = (−cₙ {4})1/4 for n = 2 and 3, with the goal of extracting long-range multiparticle azimuthal correlation signals and suppressing the short-range correlations. The values of cₙ {4} are obtained as a function of the average number of charged particles per event, (Nch), using the recently proposed two-subevent and three-subevent cumulant methods, and compared with results obtained with the standard cumulant method. The standard method is found to be strongly biased by short-range correlations, which originate mostly from jets with a positive contribution to cₙ {4}. The three- subevent method, on the other hand, is found to be least sensitive to short-range correlations. The three-subevent method gives a negative c₂ {4}, and therefore a well-defined v₂ {4}, nearly independent of (Nch) which implies that the long-range multiparticle azimuthal correlations persist to events with low multiplicity. Furthermore, v₂ {4} is found to be smaller than the v₂ {2} measured using the two-particle correlation method, as expected for long-range collective behavior. Finally, the measured values of v₂ {4} and v₂ {2} are used to estimate the number of sources relevant for the initial eccentricity in the collision geometry. The results based on the subevent cumulant technique provide direct evidence, in small collision systems, for a long-range collectivity involving many particles distributed across a broad rapidity interval.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".