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Possibility to disentangle anisotropic flow, flow fluctuation, and nonflow assuming Gaussian fluctuations

Nuclear Experiment · Submitted 24 Jan 2011

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show that such disentanglement is possible when the flow fluctuations are large, comparable to the average flow magnitude.

how it is stated · asserted 0.97 read the record →

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arXiv ID1101.4646 (version 3)
Pith ID4WGNMBMD, Pith's permanent record of this paper and its review
CitationAihong Tang, Fuqiang Wang, Li Yi. "Possibility to disentangle anisotropic flow, flow fluctuation, and nonflow assuming Gaussian fluctuations." arXiv:1101.4646 (2011). https://pith.science/paper/4WGNMBMD
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,518 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.94 · human participants 0.01 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.08 · compares with prior results 0.07 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

We suggest the possibility to disentangle anisotropic flow, flow fluctuation, and nonflow using two-, four-, and six-particle azimuthal moments assuming Gaussian fluctuations. We show that such disentanglement is possible when the flow fluctuations are large, comparable to the average flow magnitude. When fluctuations are small, the disentanglement becomes difficult. We verify our results with a toy-model Monte Carlo simulation.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We suggest the possibility to disentangle anisotropic flow, flow fluctuation, and nonflow using two-, four-, and six-particle azimuthal moments assuming Gaussian fluctuations. contribution 0.98 0.76 0.67 hedged possibility 1.00
s1 We show that such disentanglement is possible when the flow fluctuations are large, comparable to the average flow magnitude. result 0.96 0.91 0.44 asserted 0.97
s2 When fluctuations are small, the disentanglement becomes difficult. limitation 0.79 0.75 0.11 asserted 0.98
s3 We verify our results with a toy-model Monte Carlo simulation. method 0.94 0.95 0.04 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.88 · evidence from simulation or computation 0.91.

What was examined, and what was not

sources read
  • arXiv listing abstract v3
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We suggest the possibility to disentangle anisotropic flow, flow fluctuation, and nonflow using two-, four-, and six-particle azimuthal moments assuming Gaussian fluctuations. We show that such disentanglement is possible when the flow fluctuations are large, comparable to the average flow magnitude. When fluctuations are small, the disentanglement becomes difficult. We verify our results with a toy-model Monte Carlo simulation.

Human review
References
How the literature uses it

10 arXiv papers cite this in 13 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 11 sentences without a confident label

Uses its method or tool 2 papers

What it cites · 7 sentences without a confident label

Uses the method or tool of 1 paper
Cites as background 8 papers

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