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Study of the hottest droplet of fluid through correlations and fluctuations of collective variables

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Pith's one-line read The thesis argues that event-by-event fluctuations of mean transverse momentum and harmonic flow in ultracentral Pb+Pb collisions are captured by a correlated two-variable Gaussian model, and that flow-vector decorrelation between…

desk verdict A competent thesis compilation of six published papers; the headline ATLAS-variance explanation is fit-based, and the skewness/kurtosis predictions are untested extrapolations. read the letter →

arxiv 2505.12961 v1 pith:4JMTGVCL submitted 2025-05-19 nucl-th hep-ph

classification nucl-thhep-ph
keywords quark-gluonplasmameantransversemomentumfluctuationsharmonicflowfactorizationbreakingdecorrelationultracentralheavy-ioncollisionsnucleardeformationinitial-state
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis argues that event-by-event fluctuations of the hot, dense fluid formed in heavy-ion collisions can be read off from correlations among a handful of bulk observables: the per-event mean transverse momentum [pT], the harmonic flow coefficients v_n, and the charged multiplicity Nch. Its central demonstration is that the surprisingly steep drop in the variance of [pT] measured by ATLAS in ultracentral Pb+Pb collisions is naturally produced by a model in which Nch and [pT] fluctuate together as a two-dimensional Gaussian, with most of the ultracentral signal coming from impact-parameter fluctuations. The same framework yields skewness and kurtosis predictions for [pT]. For flow, it shows that event-by-event fluctuations break the factorization of flow vectors between different transverse-momentum bins, and that this breaking splits roughly equally into flow-magnitude and flow-angle decorrelation—a pattern that a toy model explains. If these claims hold, the observables put new constraints on the initial state and on nuclear deformation parameters.

What carries the argument

The main object of the thesis is a family of correlation coefficients built from per-event flow vectors. The first-order factorization-breaking coefficient $r_n(p_1,p_2)=\langle V_n(p_1)V_n^*(p_2)\rangle/\sqrt{\langle v_n^2(p_1)\rangle\langle v_n^2(p_2)\rangle}$ measures how much flow vectors in two $p_T$ bins decorrelate; a momentum-averaged version $r_n(p)=\langle V_n V_n^*(p)\rangle/\sqrt{\langle v_n^2\rangle\langle v_n^2(p)\rangle}$ removes the statistics problem of needing two particles in the same bin. Second-order analogues built from $V_n^2$ allow the decorrelation to be split into magnitude and angle parts. For $[p_T]$, the workhorse is a correlated two-dimensional Gaussian $P(N_{\rm ch},[p_T])$ whose parameters are fitted to the ATLAS variance; from it the conditional variance ${\rm Var}([p_T]\mid N_{\rm ch})$ is derived, and skewness and kurtosis of $[p_T]$ are predicted. This combination of flow-vector correlators and a Gaussian moment model carries the whole argument.

What would settle it

Measure the skewness of the [pT] distribution in ultracentral Pb+Pb events; if its sign or centrality dependence disagrees with the correlated-Gaussian prediction presented in Chapter 4, the central model is ruled out even though the variance fit may look good. A second, independent check is to bin by a centrality estimator that does not rely on charged multiplicity and test the predicted impact-parameter-fluctuation contribution to Var([pT]).

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that event-by-event fluctuations of [pT] and v_n in ultracentral Pb+Pb collisions are not noise but information: they encode the same initial-state fluctuations that set the size and shape of the fireball. In the model developed here, the joint distribution of charged multiplicity Nch and [pT] is a two-dimensional Gaussian whose covariance is fixed using the ATLAS variance data, and the steep fall of Var([pT]) with centrality is then a direct consequence, with impact-parameter fluctuations playing the dominant role. The thesis further claims that fluctuations of harmonic flow can be probed by factorization-breaking coefficients between flow vectors in different pT bins, and that the resulting decorrelation decomposes into roughly equal flow-magnitude and flow-angle decorrelation. Correlations of [pT] with $v_n^{2}$, including higher-order symmetric cumulants, are presented as maps of initial-state shape-size correlations, and the same observables are shown to constrain nuclear deformation in U+U collisions.

Load-bearing premise

The result collapses if the joint event-by-event distribution of Nch and [pT] is not close to a two-dimensional Gaussian: the model's variance explanation and its skewness and kurtosis predictions both come from that Gaussian, and its parameters are fitted to a single experimental variance curve.

Editorial extensions

If this is right

  • The steep fall of Var([pT]) in ultracentral Pb+Pb becomes a natural consequence of impact-parameter fluctuations in a correlated Gaussian model, so no exotic source of [pT] fluctuation is needed.
  • The predicted skewness and kurtosis of [pT] can be checked against future measurements and would give independent constraints on the initial-state size-shape correlation.
  • The factorization-breaking coefficients r_n(p), r_{n;2}(p), and F_n(p) provide experimental probes of flow fluctuations, with the near-equal split between magnitude and angle decorrelation as a testable signature.
  • Pearson correlations between [pT] and v_n^2, together with higher-order symmetric cumulants, map initial-state shape-size correlations and add constraints beyond the usual flow measurements.
  • The same correlation and fluctuation observables, applied to U+U collisions, can place robust constraints on nuclear deformation parameters.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct extension would be to measure the conditional variance Var([pT]|Nch) at fixed charged multiplicity; the correlated-Gaussian model makes a precise prediction for this conditional moment, and it is not the same as the centrality-binned variance shown by ATLAS, so it would be an independent test.
  • If the same two-dimensional Gaussian construction is applied to smaller systems such as p+Pb or low-multiplicity Pb+Pb, it would likely break down; observing where it breaks could quantify how non-Gaussian the initial-state fluctuations become.
  • If the near-equal split between magnitude and angle decorrelation persists event-class by event-class, experimental analyses could save statistics by measuring only the easier magnitude correlator and reconstructing the vector decorrelation from it; if it fails, that itself signals correlations between flow magnitude and angle that the toy model averages away.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This doctoral thesis studies event-by-event fluctuations and correlations of collective observables in ultrarelativistic heavy-ion collisions, focusing on mean transverse momentum per particle [pT] and harmonic flow coefficients v_n. The main results are organized in three chapters: (i) factorization-breaking coefficients between flow vectors in different p_T bins, including experimentally feasible versions with one momentum-averaged flow, and a decomposition into flow-magnitude and flow-angle decorrelation; (ii) an explanation of the steep fall of Var([pT]) in ultracentral Pb+Pb collisions reported by ATLAS, based on a correlated two-dimensional Gaussian model for (N_ch, [pT]) with impact-parameter fluctuations, together with predicted skewness and kurtosis; and (iii) Pearson correlation coefficients between [pT] and v_n^2, higher-order normalized and symmetric cumulants, momentum-dependent correlations sensitive to nucleon width, and applications to nuclear deformation. The results are obtained with standard Glauber or TRENTo initial conditions followed by 2+1D viscous hydrodynamic evolution with MUSIC, and are compared with ALICE and ATLAS data.

Significance. If the results hold, the thesis makes several useful contributions: the factorization-breaking coefficients in Sec. 3.2 provide new, experimentally accessible observables that separate magnitude and angle decorrelation; the skewness and kurtosis predictions in Sec. 4.2 are falsifiable and would constrain the initial-state mechanism behind [pT] fluctuations; and the correlation/cumulant constructions in Chapter 5 give additional handles on initial-state granularity and nuclear deformation. The thesis is based on a series of published papers, and it reproduces the relevant published figures with the hydrodynamic setups. The use of explicit model-to-data comparisons for the factorization-breaking coefficients, and the analytic derivations for the correlated Gaussian variance in Chapter 4, are strengths. However, the central explanatory claim of Chapter 4 is weakened by the fact that the Gaussian parameters are fitted to the very ATLAS variance the model is said to explain, and the advertised skewness/kurtosis predictions are extrapolations of that fitted ansatz.

major comments (3)
  1. [Sec. 4.1.3–4.1.5] The abstract's central claim that the model "can explain the steep fall" of Var([pT]) is not supported at the level of an explanation, because the parameters of the two-dimensional Gaussian (multiplicity width, [pT] width, and correlation) are fitted to the ATLAS variance data in Sec. 4.1.5. Agreement with the fitted variance is therefore partly by construction. Moreover, for a bivariate Gaussian, Var([pT]|N_ch) is independent of N_ch, so the observed centrality/ultracentral fall must be generated by allowing the parameters to vary with N_ch; the fit has enough freedom to absorb the effect without independently constraining the physical mechanism. Please reframe the claim as a successful two-parameter description and provide an independent cross-check, for example by fixing the Gaussian parameters from separate moments or from a different centrality range before comparing with the steep fall.
  2. [Sec. 4.2.1–4.2.3] The skewness and kurtosis predictions are advertised as "robust" (abstract and Sec. 4.2.3), but a bivariate Gaussian has zero skewness and kurtosis equal to 3; all non-Gaussian content in the results comes from the assumed distribution of impact parameters and the assumed linear relation between N_ch and [pT]. The variance fit does not constrain the tails of these distributions, so the skewness and kurtosis predictions are untested extrapolations. To make the claim robust, the authors should test sensitivity to alternative impact-parameter distributions or centrality-selection prescriptions, or compare directly with experimental data; otherwise the predictions should be presented as model-dependent estimates rather than robust predictions.
  3. [Sec. 3.2.2, Eq. (3.61) and (3.68)] The statement that flow-vector decorrelation "can be attributed to equal contributions from the flow magnitude and flow angle decorrelation" is formulated as a general result, but the evidence is an approximate equality observed in the specific Glauber+MUSIC and TRENTo+MUSIC calculations and in a toy model. The thesis itself notes that non-flow correlations can modify the comparison with data, and the decomposition into equal halves may depend on the harmonic, centrality, and the v_n^4 weighting used in the angle correlator. Please qualify this as a model-level approximate relation rather than a universal property, or provide a derivation that states the conditions under which the relation holds.
minor comments (4)
  1. [Fig. 3.13 caption] The caption refers to "Fig. 3.59" where the intended cross-reference appears to be a later figure in the same section; this should be corrected.
  2. [Throughout] The TRENTo model is spelled inconsistently as "TRENTo" and "TRENTO"; please unify the notation. Also, the symbol p is used both for transverse momentum and for the TRENTo reduced-thickness parameter, which is confusing in places; a distinct symbol for the model parameter would improve readability.
  3. [Sec. 2.6.1] The sentence introducing the TRENTo acronym is garbled: "TRENTo which reads asReduced Thickness Event-by-event Nuclear Topology" is missing punctuation and a space. Please revise the sentence.
  4. [Eq. (3.62) and Fig. 3.16] The comparison between the factorization-breaking coefficient and the Pearson correlation coefficient would be clearer if the figure legend explicitly identified which curve corresponds to Eq. (3.62) and which to Eq. (3.60); the current caption requires the reader to infer this from the text.

Circularity Check

1 steps flagged · score 4.0 of 10

The steep-fall explanation for Var([pT]) is a fit to the ATLAS variance data, so presenting it as an explanation is partly circular; the skewness/kurtosis and factorization-breaking predictions remain independent.

  1. fitted input called prediction [Abstract and Chapter 4 outline (Secs. 4.1.3–4.1.5, App. B.2)]
    "We study fluctuations of mean transverse momentum per particle ([pT]) in ultra-central collisions and show that our model can explain the steep fall of its variance observed by the ATLAS collaboration. [...] We perform a model fit to the ATLAS data and based on our fit results we provide crucial physical argument that could be responsible behind such phenomena."

    The two-dimensional Gaussian model of (Nch, [pT]) is calibrated by fitting the ATLAS Var([pT]) data, as stated in the outline ('We perform a model fit to the ATLAS data'). The abstract then presents the reproduction of that same fitted variance as an explanation ('our model can explain the steep fall of its variance'). The fall is therefore not independently derived or predicted; it is the input of the fit. The 'predictions for mean <delta pT>' (Sec. 4.1.5) are also determined by the same fitted covariance parameters, so they are constrained consequences of the fit rather than free predictions. The genuinely independent content is the skewness/kurtosis extrapolation and the factorization-breaking analyses, which are not fitted to the variance data.

full rationale

Most of the thesis is a set of self-contained definitions and model calculations: factorization-breaking coefficients are defined as correlation coefficients of flow vectors, the equal magnitude/angle decorrelation follows from a toy model and is compared with ALICE data, and the Pearson-correlation and deformation studies are predictions checked against external measurements. No load-bearing uniqueness theorem or self-citation chain is invoked. The only substantial circularity concern is the Chapter 4 variance claim: the correlated Gaussian parameters are fitted to the ATLAS Var([pT]) data, so the statement that the model 'explains the steep fall' is a calibration statement rather than an independent prediction. This does not invalidate the skewness and kurtosis predictions, which are extrapolations to moments not used in the fit, but the variance explanation itself reduces in part to its own input. Score 4 reflects this partial circularity while acknowledging the independent predictive content elsewhere in the thesis.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard hydrodynamic modeling plus a fitted Gaussian fluctuation model. The main free parameters are the Gaussian parameters fitted to ATLAS data and the TRENTo/hydro parameters taken from defaults or prior constraints. No invented physical entities such as new particles or forces appear; the new objects are correlation observables, which are mathematical constructions rather than physical entities.

free parameters (4)
  • Correlated Gaussian parameters for (Nch, [pT]) = Not specified in the excerpt; fitted to ATLAS variance data
    Chapter 4 fits the two-dimensional Gaussian model to ATLAS Var([pT]) to reproduce the steep fall; these centrality-dependent parameters carry the central variance claim.
  • TRENTo reduced thickness parameter p = 0 (default)
    TRENTo initial conditions use p=0 unless otherwise stated; this choice affects the granularity and correlation predictions in Chapters 3-6.
  • Nucleon Gaussian width w in TRENTo = 0.6 fm default; varied to test sensitivity
    Section 5.2.2 shows the momentum-dependent Pearson correlator is sensitive to w; the value is chosen by hand from the TRENTo default rather than fitted here.
  • Shear viscosity to entropy ratio eta/s = 0.08, with 0.12 and 0.16 variations
    MUSIC simulations throughout use eta/s=0.08; variations are shown in Sec. 3.2.3 to test sensitivity of mixed-flow correlations.
assumptions (4)
  • domain assumption Boost-invariant 2+1D viscous hydrodynamics is sufficient for the studied central-rapidity observables.
    Secs. 2.6.3 and 'Simulation set-up' state that all results use boost-invariant MUSIC with no pre-equilibrium phase and no hadronic afterburner, which is a standard but nontrivial modeling choice.
  • domain assumption Initial state models (MC Glauber, TRENTo) capture the event-by-event fluctuations that drive final-state correlations.
    Chapters 3-6 rely on these initial conditions to generate flow fluctuations; if the models miss relevant fluctuation sources, the factorization-breaking and cumulant predictions fail.
  • domain assumption Residual non-flow correlations are either negligible or identifiable by comparing model and data.
    Chapter 3 attributes high-pT discrepancies in r2;2 and r_v2^2 to non-flow, but the quantitative non-flow contribution is not subtracted in the model-data comparisons.
  • ad hoc to paper The joint event-by-event distribution of Nch and [pT] is a two-dimensional Gaussian.
    Introduced in Sec. 4.1.3 to derive Var([pT]|Nch) and then skewness and kurtosis; the Gaussian shape is fitted to ATLAS variance data rather than derived from first principles.

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Pith. "Pith review of Study of the hottest droplet of fluid through correlations and fluctuations of collective variables." pith.science (2026). https://pith.science/paper/4JMTGVCL

@misc{pith2026250512961,
  author       = {Pith},
  title        = {Pith review of: Study of the hottest droplet of fluid through correlations and fluctuations of collective variables},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4JMTGVCL}},
  note         = {Machine review of arXiv:2505.12961}
}
abstract

In this thesis, we focus on the fluctuations and correlations of the collective observables such as the mean transverse momentum per particle ($[p_T]$) and harmonic flow coefficients ($v_n$) of particles produced in the ultrarelativistic heavy-ion collisions at RHIC and the LHC. Specifically, we show that the fluctuations of harmonic flow can be probed by the factorization-breaking coefficients between flow vectors in different $p_T$-bins. Experimental difficulty can be reduced by taking one of the flow vectors momentum averaged. Fluctuations cause a decorrelation between the flow vectors, which can be attributed to equal contributions from the flow magnitude and flow angle decorrelation. We study fluctuations of mean transverse momentum per particle ($[p_T]$) in ultra-central collisions and show that our model can explain the steep fall of its variance observed by the ATLAS collaboration. We also present robust predictions for the skewness and kurtosis, and highlight the role of impact parameter fluctuations in ultracentral collisions. We study the Pearson correlation coefficients between $[p_T]$ and $v_n^2$, which can map the initial state correlations between the shape and size of the fireball. We show that higher order normalized and symmetric cumulants between these observables can be constructed, which put useful additional constraints on the initial state properties. Furthermore, we study the momentum dependent Pearson correlation between $[p_T]$ and the transverse momentum dependent flow. It shows sensitivity to the Gaussian width of the nucleon at the initial state. Finally, we show that such correlations and fluctuations of collective observables can be used to study nuclear deformation and put robust constraints on their deformation parameters through high energy nuclear collisions.

Figures

Figures reproduced from arXiv: 2505.12961 by the authors.

Figure 1
Figure 1. Pictorial representation of Pb+Pb collision at the LHC and formation of the deconfined state [PITH_FULL_IMAGE:figures/full_fig_p014_1.png] view at source ↗
Figure 1
Figure 1. The aerial view of the Relativistic Heavy Ion Collider (RHIC), located at the Brookhaven [PITH_FULL_IMAGE:figures/full_fig_p015_1.png] view at source ↗
Figure 1
Figure 1. The aerial view of the Large Hadron Collider (LHC), located at CERN near the France [PITH_FULL_IMAGE:figures/full_fig_p016_1.png] view at source ↗
Figures from the paper (73 more)
Figure 1
Figure 1. Figure 1: Collective flow in ultrarelativistic heavy-ion collision seen from the [PITH_FULL_IMAGE:figures/full_fig_p018_1.png]
Figure 2
Figure 2. Figure 2: Schematic representation of the geometry of ultrarelativistic heavy-ion collision (Pb+Pb) [PITH_FULL_IMAGE:figures/full_fig_p024_2.png]
Figure 2
Figure 2. Figure 2: Space-time representation of the collision in (t,z) plane. The vertical axis represents time and [PITH_FULL_IMAGE:figures/full_fig_p026_2.png]
Figure 2
Figure 2. Figure 2: Woods-Saxon density distribution for lead (red) , copper (blue) and oxygen (green) nucleus. [PITH_FULL_IMAGE:figures/full_fig_p028_2.png]
Figure 2
Figure 2. Figure 2: Schematic representation of Pb+Pb collision on the transverse plane at [PITH_FULL_IMAGE:figures/full_fig_p029_2.png]
Figure 2
Figure 2. Figure 2: Final state multiplicity distribution ( [PITH_FULL_IMAGE:figures/full_fig_p031_2.png]
Figure 2
Figure 2. Figure 2: Schematic representation of the geometry of optical Glauber model with longitudinal (b) and [PITH_FULL_IMAGE:figures/full_fig_p032_2.png]
Figure 2
Figure 2. Figure 2: The running coupling of QCD, as a function of energy (momentum) scale [PITH_FULL_IMAGE:figures/full_fig_p050_2.png]
Figure 2
Figure 2. Figure 2: Schematic representation of different stages in heavy-ion collision along with the timeline for [PITH_FULL_IMAGE:figures/full_fig_p052_2.png]
Figure 2
Figure 2. Figure 2: Space-time diagram of different stages of heavy-ion collision. There exist a preequilibrium [PITH_FULL_IMAGE:figures/full_fig_p053_2.png]
Figure 2
Figure 2. Figure 2: Reduced thickness in the collision of two nucleons at some non-zero impact parameter along [PITH_FULL_IMAGE:figures/full_fig_p057_2.png]
Figure 2
Figure 2. Figure 2: Schematic representation of the QCD phase diagram. It is shown that the phase transition and [PITH_FULL_IMAGE:figures/full_fig_p063_2.png]
Figure 3
Figure 3. Figure 3: Schematic representation of the almond shaped fireball formation in a non-central Pb+Pb [PITH_FULL_IMAGE:figures/full_fig_p068_3.png]
Figure 3
Figure 3. Figure 3: Pictorial depiction of the reaction plane and participant plane in a collision. The area with [PITH_FULL_IMAGE:figures/full_fig_p070_3.png]
Figure 3
Figure 3. Figure 3: Pictorial representation of the participant eccentricity harmonics. The principal axes or [PITH_FULL_IMAGE:figures/full_fig_p071_3.png]
Figure 3
Figure 3. Figure 3: Event-averaged charged-particle multiplicity spectra measured by the ALICE (left) and [PITH_FULL_IMAGE:figures/full_fig_p072_3.png]
Figure 3
Figure 3. Figure 3: Schematic representation of the origin of elliptic flow in a non central heavy-ion collision. [PITH_FULL_IMAGE:figures/full_fig_p075_3.png]
Figure 3
Figure 3. Figure 3: Scatter plot between [PITH_FULL_IMAGE:figures/full_fig_p076_3.png]
Figure 3
Figure 3. Figure 3: Scatter plot between [PITH_FULL_IMAGE:figures/full_fig_p077_3.png]
Figure 3
Figure 3. Figure 3: Measurement of differential and integrated flow using multi-particle cumulant method. The [PITH_FULL_IMAGE:figures/full_fig_p081_3.png]
Figure 3
Figure 3. Figure 3: Pictorial representation of fluctuations at the initial state, generated from the MC Glauber [PITH_FULL_IMAGE:figures/full_fig_p085_3.png]
Figure 3
Figure 3. Figure 3: The factorization-breaking coefficient between elliptic flow vectors in two different transverse [PITH_FULL_IMAGE:figures/full_fig_p088_3.png]
Figure 3
Figure 3. Figure 3: Factorization-breaking coefficients between flow vectors squared (left) and flow magnitude [PITH_FULL_IMAGE:figures/full_fig_p090_3.png]
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Figure 3. Figure 3: The factorization-breaking coefficient between transverse momentum averaged ( [PITH_FULL_IMAGE:figures/full_fig_p091_3.png]
Figure 3
Figure 3. Figure 3: Factorization-breaking coefficients between momentum averaged ( [PITH_FULL_IMAGE:figures/full_fig_p092_3.png]
Figure 3
Figure 3. Figure 3: Flow vector squared factorization-breaking coefficients between momentum averaged and [PITH_FULL_IMAGE:figures/full_fig_p093_3.png]
Figure 3
Figure 3. Figure 3: Factorization-breaking coefficients for the flow magnitude squared between transverse [PITH_FULL_IMAGE:figures/full_fig_p094_3.png]
Figure 3
Figure 3. Figure 3: Comparison between different definitions of flow magnitude factorization-breaking coeffi [PITH_FULL_IMAGE:figures/full_fig_p095_3.png]
Figure 3
Figure 3. Figure 3: Flow angle decorrelation as a function of the transverse momentum for the elliptic flow in [PITH_FULL_IMAGE:figures/full_fig_p097_3.png]
Figure 3
Figure 3. Figure 3: Flow angle decorrelation for the triangular flow with [PITH_FULL_IMAGE:figures/full_fig_p098_3.png]
Figure 3
Figure 3. Figure 3: Left: Comparison between different definitions of flow angle correlation along with the [PITH_FULL_IMAGE:figures/full_fig_p099_3.png]
Figure 3
Figure 3. Figure 3: Mixed-flow correlation between [PITH_FULL_IMAGE:figures/full_fig_p101_3.png]
Figure 3
Figure 3. Figure 3: Correlation coefficients between flow vector squared [PITH_FULL_IMAGE:figures/full_fig_p102_3.png]
Figure 3
Figure 3. Figure 3: Flow angle decorrelation between [PITH_FULL_IMAGE:figures/full_fig_p103_3.png]
Figure 3
Figure 3. Figure 3: Centrality dependence of the flow angle correlation between [PITH_FULL_IMAGE:figures/full_fig_p104_3.png]
Figure 4
Figure 4. Figure 4: Variance of the transverse momentum per particle [PITH_FULL_IMAGE:figures/full_fig_p109_4.png]
Figure 4
Figure 4. Figure 4: Left: Pictorial depiction of Pb+Pb collisions at fixed multiplicity but different impact [PITH_FULL_IMAGE:figures/full_fig_p110_4.png]
Figure 4
Figure 4. Figure 4: Histogram of the charge particle multiplicity [PITH_FULL_IMAGE:figures/full_fig_p113_4.png]
Figure 4
Figure 4. Figure 4: Joint distribution of [PITH_FULL_IMAGE:figures/full_fig_p118_4.png]
Figure 4
Figure 4. Figure 4: Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p120_4.png]
Figure 4
Figure 4. Figure 4: Distribution of [PITH_FULL_IMAGE:figures/full_fig_p121_4.png]
Figure 4
Figure 4. Figure 4: Probability distribution of [PITH_FULL_IMAGE:figures/full_fig_p124_4.png]
Figure 4
Figure 4. Figure 4: Predictions for the standardized skewness as a function of [PITH_FULL_IMAGE:figures/full_fig_p128_4.png]
Figure 4
Figure 4. Figure 4: Predictions for average [PITH_FULL_IMAGE:figures/full_fig_p129_4.png]
Figure 4
Figure 4. Figure 4: Measurement of the standardized (left) and intensive (right) skewness of [PITH_FULL_IMAGE:figures/full_fig_p130_4.png]
Figure 5
Figure 5. Figure 5: The Pearson correlation coefficient between the mean transverse momentum per particle [PITH_FULL_IMAGE:figures/full_fig_p135_5.png]
Figure 5
Figure 5. Figure 5: Left: The Pearson correlation coefficient between the mean transverse momentum per particle [PITH_FULL_IMAGE:figures/full_fig_p136_5.png]
Figure 5
Figure 5. Figure 5: Left: Scatter plot between event-by-event mean transverse momentum per particle and [PITH_FULL_IMAGE:figures/full_fig_p139_5.png]
Figure 5
Figure 5. Figure 5: Left: Normalized symmetric cumulant between mean transverse momentum per particle and [PITH_FULL_IMAGE:figures/full_fig_p141_5.png]
Figure 5
Figure 5. Figure 5: Third order normalized symmetric cumulant between mean transverse momentum per particle, [PITH_FULL_IMAGE:figures/full_fig_p142_5.png]
Figure 5
Figure 5. Figure 5: Third order normalized symmetric cumulant between [PITH_FULL_IMAGE:figures/full_fig_p143_5.png]
Figure 5
Figure 5. Figure 5: Left: Third order normalized symmetric cumulant between mean transverse momentum [PITH_FULL_IMAGE:figures/full_fig_p144_5.png]
Figure 5
Figure 5. Figure 5: Third order scaled symmetric cumulant between mean transverse momentum per particle, [PITH_FULL_IMAGE:figures/full_fig_p145_5.png]
Figure 5
Figure 5. Figure 5: Left: Momentum dependent Pearson correlation between mean transverse momentum per [PITH_FULL_IMAGE:figures/full_fig_p148_5.png]
Figure 5
Figure 5. Figure 5: Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p149_5.png]
Figure 5
Figure 5. Figure 5: Left: Pearson correlation coefficient [PITH_FULL_IMAGE:figures/full_fig_p151_5.png]
Figure 5
Figure 5. Figure 5: Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p153_5.png]
Figure 5
Figure 5. Figure 5: Momentum dependent correlation coefficient between [PITH_FULL_IMAGE:figures/full_fig_p154_5.png]
Figure 5
Figure 5. Figure 5: Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p155_5.png]
Figure 5
Figure 5. Figure 5: Normalized covariance between mean transverse momentum [PITH_FULL_IMAGE:figures/full_fig_p157_5.png]
Figure 5
Figure 5. Figure 5: Left: Ratio of the momentum dependent and momentum independent covariance between [PITH_FULL_IMAGE:figures/full_fig_p158_5.png]
Figure 6
Figure 6. Figure 6: Deformed uranium nucleus having prolate structure with [PITH_FULL_IMAGE:figures/full_fig_p163_6.png]
Figure 6
Figure 6. Figure 6: Pictorial representation of the body-to-body and tip-to-tip collision of deformed uranium [PITH_FULL_IMAGE:figures/full_fig_p164_6.png]
Figure 6
Figure 6. Figure 6: Factorization-breaking coefficients of elliptic flow vector squared [PITH_FULL_IMAGE:figures/full_fig_p168_6.png]
Figure 6
Figure 6. Figure 6: Left: Flow magnitude squared factorization breaking coefficient for the elliptic flow [PITH_FULL_IMAGE:figures/full_fig_p169_6.png]
Figure 6.5
Figure 6.5. Figure 6.5: Momentum dependent mixed flow correlation between [PITH_FULL_IMAGE:figures/full_fig_p170_6_5.png]
Figure 6
Figure 6. Figure 6: Comparison of the deformation effect through flow vector factorization breaking coefficient [PITH_FULL_IMAGE:figures/full_fig_p171_6.png]
Figure 6
Figure 6. Figure 6: The Pearson correlation coefficients between mean transverse momentum per particle and [PITH_FULL_IMAGE:figures/full_fig_p172_6.png]
Figure 6
Figure 6. Figure 6: Left: Pearson correlation coefficient between mean transverse momentum [PITH_FULL_IMAGE:figures/full_fig_p173_6.png]
Figure 6
Figure 6. Figure 6: Left: Third order normalized symmetric cumulant between mean transverse momentum, [PITH_FULL_IMAGE:figures/full_fig_p174_6.png]
Figure 6
Figure 6. Figure 6: Fourth order normalized symmetric cumulant between mean transverse momentum, multi [PITH_FULL_IMAGE:figures/full_fig_p175_6.png]
Figure 7
Figure 7. Figure 7: Pictorial representation of the structure of xenon-129 nucleus with three possible configura [PITH_FULL_IMAGE:figures/full_fig_p180_7.png]
Figure 7
Figure 7. Figure 7: Pictorial depiction of the [PITH_FULL_IMAGE:figures/full_fig_p182_7.png]

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