pith. machine review for the scientific record. sign in

arxiv: 2604.15737 · v1 · submitted 2026-04-17 · ✦ hep-ph

Recognition: unknown

Bayesian inference constraints on jet quenching across centrality, beam energy, and observable classes in LHC heavy-ion collisions

Beomkyu Kim, Dongguk Kim, Dongjo Kim, Jeongsu Bok

Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:46 UTC · model grok-4.3

classification ✦ hep-ph
keywords acrosscollisionsconstraintsenergypredictivebayesianbeamcentrality
0
0 comments X

The pith

Bayesian posteriors from JETSCAPE jet-quenching model are largely compatible across centrality but exhibit shifts across beam energy and observable class, with varying ability to predict complementary datasets.

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

Heavy-ion collisions at the LHC create a hot soup of quarks and gluons called the quark-gluon plasma. High-energy particles called jets lose energy as they travel through this soup, a process known as jet quenching. Physicists use models with adjustable parameters to describe how much energy is lost. This work takes one such model with six free parameters and fits it separately to different slices of the data: different collision centralities, two different beam energies, and two broad classes of measurements (charged hadrons versus inclusive jets). The authors then compare the resulting probability distributions for the parameters. They also test how well each fitted distribution can predict the data it was not trained on. The main finding is that the parameter ranges stay similar when the data are split by how central the collision is, but they shift noticeably when the data are split by beam energy or by whether the measurement focuses on hadrons or on full jets. Predictive accuracy on the unused data also varies across these splits.

Core claim

We find that centrality-dependent posteriors are largely compatible, whereas beam-energy and observable-class splits exhibit moderate shifts within overlapping credible regions, indicating that posterior overlap alone does not guarantee predictive universality. This is further examined by propagating subset posteriors to complementary datasets without refitting, where predictive performance varies across subsets.

Load-bearing premise

The six-parameter JETSCAPE effective energy-loss model is assumed to capture the dominant physics across all centrality, energy, and observable classes without needing additional parameters or different functional forms for each subset.

Figures

Figures reproduced from arXiv: 2604.15737 by Beomkyu Kim, Dongguk Kim, Dongjo Kim, Jeongsu Bok.

Figure 2
Figure 2. Figure 2: FIG. 2. Emulator validation (Method 1): partial leave-one [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Closure-test posterior panel for 9 randomly selected [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Emulator validation (Method 2): full-performance [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Posterior distributions of calibration parameters from [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Their median trends remain close over the dis [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Representative posterior predictions for the fitted [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Posterior estimates of ˆq [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Representative cross-prediction examples. Top: cen [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Mean sensitivity map for the calibrated observables, evaluated around the MAP point with a relative parameter [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Leading-hadron-selected full-jet [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14. 50 design points sampled within the prior parameter [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: FIG. 15. Correlation matrix among calibration parameters. [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗
Figure 18
Figure 18. Figure 18: FIG. 18. Additional posterior-predictive central panels not [PITH_FULL_IMAGE:figures/full_fig_p019_18.png] view at source ↗
Figure 16
Figure 16. Figure 16: FIG. 16. Additional ALICE and ATLAS prior distributions [PITH_FULL_IMAGE:figures/full_fig_p019_16.png] view at source ↗
Figure 19
Figure 19. Figure 19: FIG. 19. Additional posterior-predictive mid-central panels [PITH_FULL_IMAGE:figures/full_fig_p019_19.png] view at source ↗
Figure 17
Figure 17. Figure 17: FIG. 17. Additional ALICE and ATLAS prior distributions [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 20
Figure 20. Figure 20: FIG. 20. Additional posterior-predictive mid-central panels [PITH_FULL_IMAGE:figures/full_fig_p019_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: FIG. 21. Additional Method 1 central panels not shown in the [PITH_FULL_IMAGE:figures/full_fig_p020_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: FIG. 22. Additional Method 1 mid-central panels for PbPb [PITH_FULL_IMAGE:figures/full_fig_p020_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: FIG. 23. Additional Method 1 mid-central panels for PbPb [PITH_FULL_IMAGE:figures/full_fig_p020_23.png] view at source ↗
read the original abstract

Jet quenching in heavy-ion collisions probes parton energy loss in the quark--gluon plasma (QGP), but the extracted transport properties may not be universally constrained across centrality, beam energy, and observable class. In this work, we perform an analysis of the compatibility and predictive transferability of Bayesian constraints obtained from a six-parameter JETSCAPE effective energy-loss model across these subsets. The model is calibrated to charged-hadron and inclusive-jet data from ALICE, ATLAS, and CMS in PbPb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ and $2.76$ TeV. We find that centrality-dependent posteriors are largely compatible, whereas beam-energy and observable-class splits exhibit moderate shifts within overlapping credible regions, indicating that posterior overlap alone does not guarantee predictive universality. This is further examined by propagating subset posteriors to complementary datasets without refitting, where predictive performance varies across subsets. These results indicate that different observables probe distinct aspects of jet--medium interactions and motivate leading-hadron-selected jet observables to bridge hadron-biased and jet-inclusive constraints.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Circularity Check

0 steps flagged

No significant circularity; transferability tests are independent of fitting inputs

full rationale

The paper calibrates a fixed six-parameter JETSCAPE energy-loss model to experimental datasets split by centrality, beam energy, and observable class, then propagates the resulting posteriors to held-out complementary datasets without refitting to assess predictive performance. This procedure is a standard cross-validation check whose outcomes (varying predictive accuracy) are empirical results, not definitions or tautologies. No equations reduce a claimed prediction to the fit by construction, no self-citation chain bears the central claim, and the model form is treated as an external assumption rather than derived from the target observables. The abstract and described workflow remain self-contained against the external LHC data.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the six-parameter JETSCAPE effective energy-loss model is an adequate description of the data across all partitions and that the Bayesian likelihood is correctly specified; no independent evidence for these modeling choices is supplied in the abstract.

free parameters (1)
  • six-parameter JETSCAPE effective energy-loss model parameters
    The model is calibrated to data; the six parameters are fitted quantities whose values are not derived from first principles.
axioms (1)
  • domain assumption The JETSCAPE effective energy-loss model captures the dominant jet-medium interactions across the probed kinematic ranges
    Invoked when the same parameter set is applied to all centrality, energy, and observable subsets.

pith-pipeline@v0.9.0 · 5503 in / 1410 out tokens · 37038 ms · 2026-05-10T08:46:10.249281+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

126 extracted references · 111 canonical work pages · 4 internal anchors

  1. [1]

    This improves the statistical coverage of the rare high-pT region while keeping the total computational cost manageable

    Hard-process stitching and smoothing Because the charged-hadron and inclusive-jet observ- ables used in this work extend to relatively high trans- verse momentum, the event generation is performed in ˆpT-binned samples rather than in a single fully inclusive sample. This improves the statistical coverage of the rare high-pT region while keeping the total ...

  2. [2]

    Background generation settings The event-by-event initial entropy-density profiles used for the medium background are generated with the TRENTo model [21, 22]. In the present analysis, the TRENTo events are generated in advance and stored as HDF5 files, which are then used as the initial-condition ensemble for the later medium-evolution stage. For both Pb...

  3. [3]

    For each design point, we generate 10,000 hard- scattering events for each centrality class at each colli- sion energy

    Simulation statistics and design coverage A design point denotes a unique parameter vector (Q0, τ0,A,B,C,α s) at which a full event simulation is per- formed. For each design point, we generate 10,000 hard- scattering events for each centrality class at each colli- sion energy. These are propagated through an ensem- ble of 100 distinct QCD medium backgrou...

  4. [4]

    Adamset al.(STAR), Nucl

    J. Adamset al.(STAR), Nucl. Phys.A757, 102 (2005), arXiv:nucl-ex/0501009 [nucl-ex]

    work page arXiv 2005

  5. [5]

    Adcoxet al.(PHENIX), Nucl

    K. Adcoxet al.(PHENIX), Nucl. Phys.A757, 184 (2005), arXiv:nucl-ex/0410003 [nucl-ex]

    work page arXiv 2005

  6. [6]

    Arsene et al

    I. Arseneet al.(BRAHMS), Nucl. Phys.A757, 1 (2005), arXiv:nucl-ex/0410020 [nucl-ex]

    work page arXiv 2005

  7. [7]

    B. B. Backet al.(PHOBOS), Nucl. Phys.A757, 28 (2005), arXiv:nucl-ex/0410022 [nucl-ex]

    work page arXiv 2005

  8. [8]

    Heavy Ion Collisions: The Big Picture, and the Big Questions

    W. Busza, K. Rajagopal, and W. van der Schee, Ann. Rev. Nucl. Part. Sci.68, 339 (2018), arXiv:1802.04801 [hep-ph]

    work page Pith review arXiv 2018

  9. [9]

    Properties of hot and dense matter from relativistic heavy ion collisions,

    P. Braun-Munzinger, V. Koch, T. Sch¨ afer, and J. Stachel, Phys. Rept.621, 76 (2016), arXiv:1510.00442 [nucl-th]

    work page arXiv 2016

  10. [10]

    Shuryak, Prog

    E. Shuryak, Prog. Part. Nucl. Phys.62, 48 (2009), arXiv:0807.3033 [hep-ph]

    work page arXiv 2009

  11. [11]

    Collective flow and viscosity in relativistic heavy-ion collisions

    U. Heinz and R. Snellings, Ann. Rev. Nucl. Part. Sci. 63, 123 (2013), arXiv:1301.2826 [nucl-th]

    work page Pith review arXiv 2013

  12. [12]

    Gyulassy and M

    M. Gyulassy and M. Plumer, Phys. Lett. B243, 432 (1990)

    1990

  13. [13]

    Wang and M

    X.-N. Wang and M. Gyulassy, Phys. Rev. Lett.68, 1480 (1992)

    1992

  14. [14]

    Majumder and M

    A. Majumder and M. van Leeuwen, Prog. Part. Nucl. Phys.66, 41 (2011), arXiv:1002.2206 [hep-ph]

    work page arXiv 2011

  15. [15]

    Jet quenching in high-energy heavy-ion collisions

    G.-Y. Qin and X.-N. Wang, Int. J. Mod. Phys. E24, 1530014 (2015), arXiv:1511.00790 [hep-ph]

    work page Pith review arXiv 2015

  16. [16]

    Cao and X.-N

    S. Cao and X.-N. Wang, Rept. Prog. Phys.84, 024301 (2021), arXiv:2002.04028 [hep-ph]

    work page arXiv 2021

  17. [17]

    Baier, Y

    R. Baier, Y. L. Dokshitzer, A. H. Mueller, S. Peigne, and D. Schiff, Nucl. Phys. B484, 265 (1997), arXiv:hep- ph/9608322

    work page arXiv 1997

  18. [18]

    K. M. Burkeet al.(JET), Phys. Rev. C90, 014909 (2014), arXiv:1312.5003 [nucl-th]

    work page Pith review arXiv 2014

  19. [19]

    Caoet al.(JETSCAPE), Determining the jet trans- port coefficient qˆ from inclusive hadron suppression measurements using Bayesian parameter estimation, Phys

    S. Caoet al.(JETSCAPE), Phys. Rev. C104, 024905 (2021), arXiv:2102.11337 [nucl-th]

    work page arXiv 2021

  20. [20]

    Fanet al.(JETSCAPE), Phys

    W. Fanet al.(JETSCAPE), Phys. Rev. C109, 064903 (2024), arXiv:2307.09641 [hep-ph]

    work page arXiv 2024

  21. [21]

    Ehlerset al., Phys

    R. Ehlerset al.(JETSCAPE), Phys. Rev. C111, 054913 (2025), arXiv:2408.08247 [hep-ph]

    work page arXiv 2025

  22. [22]

    Everettet al.(JETSCAPE), Multisystem Bayesian constraints on the transport coefficients of QCD matter, Phys

    D. Everettet al.(JETSCAPE), Phys. Rev. C103, 054904 (2021), arXiv:2011.01430 [hep-ph]

    work page arXiv 2021

  23. [23]

    An Introduction to PYTHIA 8.2

    T. Sj¨ ostrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, and P. Skands, Comput. Phys. Commun.191, 159 (2015), arXiv:1410.3012 [hep-ph]

    work page internal anchor Pith review arXiv 2015

  24. [24]

    J. S. Moreland, J. E. Bernhard, and S. A. Bass, Phys. Rev. C92, 011901 (2015), arXiv:1412.4708 [nucl-th]

    work page arXiv 2015

  25. [25]

    J. E. Bernhard, J. S. Moreland, S. A. Bass, J. Liu, and U. Heinz, Phys. Rev. C94, 024907 (2016), arXiv:1605.03954 [nucl-th]

    work page arXiv 2016

  26. [26]

    Song and U

    H. Song and U. W. Heinz, Phys. Rev. C77, 064901 (2008), arXiv:0712.3715 [nucl-th]

    work page arXiv 2008

  27. [27]

    PHOTOS: A Universal Monte Carlo for QED radiative corrections. Version 2.0

    S. Acharyaet al.(ALICE), JHEP11(11), 013, arXiv:1802.09145 [nucl-ex]

    work page arXiv

  28. [28]

    Aadet al.(ATLAS), JHEP09(09), 050, arXiv:1504.04337 [hep-ex]

    G. Aadet al.(ATLAS), JHEP09(09), 050, arXiv:1504.04337 [hep-ex]

    work page arXiv

  29. [29]

    Charged-particle nuclear modification factors in PbPb and pPb collisions at √ sN N = 5.02 TeV

    V. Khachatryanet al.(CMS), JHEP04(04), 039, arXiv:1611.01664 [nucl-ex]

    work page arXiv

  30. [30]

    Chatrchyanet al.(CMS), Eur

    S. Chatrchyanet al.(CMS), Eur. Phys. J. C72, 1945 (2012), arXiv:1202.2554 [nucl-ex]

    work page arXiv 1945

  31. [31]

    Measurements of inclusive jet spectra in pp and central Pb-Pb collisions at √ sNN = 5.02 TeV

    S. Acharyaet al.(ALICE), Phys. Rev. C101, 034911 (2020), arXiv:1909.09718 [nucl-ex]

    work page arXiv 2020

  32. [32]

    Aaboudet al.(ATLAS), Phys

    M. Aaboudet al.(ATLAS), Phys. Lett. B790, 108 (2019), arXiv:1805.05635 [nucl-ex]

    work page arXiv 2019

  33. [33]

    Aadet al.(ATLAS), Phys

    G. Aadet al.(ATLAS), Phys. Rev. Lett.114, 072302 (2015), arXiv:1411.2357 [hep-ex]

    work page arXiv 2015

  34. [34]

    A. M. Sirunyanet al.(CMS), arXiv (2021), arXiv:2102.13080 [hep-ex]

    work page arXiv 2021

  35. [35]

    Khachatryanet al.(CMS), Phys

    V. Khachatryanet al.(CMS), Phys. Rev. C96, 015202 (2017), arXiv:1609.05383 [nucl-ex]

    work page arXiv 2017

  36. [36]

    Casalderrey-Solana, Z

    J. Casalderrey-Solana, Z. Hulcher, G. Milhano, D. Pab- los, and K. Rajagopal, Phys. Rev. C99, 051901 (2019)

    2019

  37. [37]

    Y.-L. Du, D. Pablos, and K. Tywoniuk, Phys. Rev. Lett. 128, 012301 (2022)

    2022

  38. [38]

    Y. He, S. Cao, W. Chen, T. Luo, L.-G. Pang, and X.-N. Wang, Phys. Rev. C99, 054911 (2019)

    2019

  39. [39]

    M. D. McKay, R. J. Beckman, and W. J. Conover, Tech- nometrics21, 239 (1979)

    1979

  40. [40]

    Tang, Journal of the American Statistical Association 88, 1392 (1993)

    B. Tang, Journal of the American Statistical Association 88, 1392 (1993)

    1993

  41. [41]

    M. D. Morris and T. J. Mitchell, Journal of Statistical Planning and Inference43, 381 (1995)

    1995

  42. [42]

    Abelevet al.(ALICE), JHEP09, 049, arXiv:1307.1249 [nucl-ex]

    B. Abelevet al.(ALICE), JHEP09, 049, arXiv:1307.1249 [nucl-ex]

    work page arXiv

  43. [43]

    Abelevet al.(ALICE), Phys

    B. Abelevet al.(ALICE), Phys. Lett. B741, 38 (2015), arXiv:1406.5463 [nucl-ex]

    work page arXiv 2015

  44. [44]

    Viscosity in Strongly Interacting Quantum Field Theories from Black Hole Physics

    P. Kovtun, D. T. Son, and A. O. Starinets, Phys. Rev. Lett.94, 111601 (2005), arXiv:hep-th/0405231 [hep-th]

    work page internal anchor Pith review arXiv 2005

  45. [45]

    J. E. Bernhard, J. S. Moreland, and S. A. Bass, Nature Physics15, 1113 (2019)

    2019

  46. [46]

    Borghini, P

    N. Borghini, P. M. Dinh, and J.-Y. Ollitrault, Phys. Rev. C64, 054901 (2001), arXiv:nucl-th/0105040

    work page arXiv 2001

  47. [47]

    S. F. Taghavi, Eur. Phys. J. C81, 652 (2021), arXiv:2005.04742 [nucl-th]

    work page arXiv 2021

  48. [48]

    Bilandzic, M

    A. Bilandzic, M. Lesch, and S. F. Taghavi, Phys. Rev. C102, 024910 (2020), arXiv:2004.01066 [nucl-ex]

    work page arXiv 2020

  49. [49]

    Bilandzic, M

    A. Bilandzic, M. Lesch, C. Mordasini, and S. F. Taghavi, arXiv (2021), arXiv:2101.05619 [physics.data-an]

    work page arXiv 2021

  50. [50]

    Mordasini, A

    C. Mordasini, A. Bilandzic, D. Karako¸ c, and S. F. Taghavi, Phys. Rev. C102, 024907 (2020), arXiv:1901.06968 [nucl-ex]

    work page arXiv 2020

  51. [51]

    Bazavovet al

    A. Bazavovet al.(HotQCD), Phys. Rev. D90, 094503 (2014), arXiv:1407.6387 [hep-lat]

    work page arXiv 2014

  52. [52]

    Borghini, P

    N. Borghini, P. M. Dinh, and J.-Y. Ollitrault, Phys. Rev. C63, 054906 (2001), arXiv:nucl-th/0007063

    work page arXiv 2001

  53. [53]

    Bilandzic, C

    A. Bilandzic, C. H. Christensen, K. Gulbrandsen, A. Hansen, and Y. Zhou, Phys. Rev. C89, 064904 (2014), arXiv:1312.3572 [nucl-ex]

    work page arXiv 2014

  54. [54]

    N. Y. Astrakhantsev, V. V. Braguta, and A. Y. Kotov, Phys. Rev. D98, 054515 (2018), arXiv:1804.02382 [hep- lat]

    work page arXiv 2018

  55. [55]

    H. B. Meyer, Phys. Rev. Lett.100, 162001 (2008), arXiv:0710.3717 [hep-lat]

    work page arXiv 2008

  56. [56]

    Astrakhantsev, V

    N. Astrakhantsev, V. Braguta, and A. Kotov, JHEP04, 101, arXiv:1701.02266 [hep-lat]

    work page arXiv

  57. [57]

    H. B. Meyer, Phys. Rev. D76, 101701 (2007), arXiv:0704.1801 [hep-lat]

    work page arXiv 2007

  58. [58]

    Bazavov, F

    A. Bazavov, F. Karsch, S. Mukherjee, and P. Pe- treczky (USQCD), Eur. Phys. J. A55, 194 (2019), arXiv:1904.09951 [hep-lat]. 23

    work page arXiv 2019

  59. [59]

    Borsanyi, Z

    S. Borsanyi, Z. Fodor, C. Hoelbling, S. D. Katz, S. Krieg, and K. K. Szabo, Phys. Lett. B730, 99 (2014), arXiv:1309.5258 [hep-lat]

    work page arXiv 2014

  60. [60]

    C. Shen, Z. Qiu, H. Song, J. Bernhard, S. Bass, and U. Heinz, Comput. Phys. Commun.199, 61 (2016), arXiv:1409.8164 [nucl-th]

    work page arXiv 2016

  61. [61]

    Acharyaet al.(ALICE), Phys

    S. Acharyaet al.(ALICE), Phys. Lett. B818, 136354 (2021), arXiv:2102.12180 [nucl-ex]

    work page arXiv 2021

  62. [62]

    Acharyaet al.(ALICE), Phys

    S. Acharyaet al.(ALICE), Phys. Rev. Lett.127, 092302 (2021), arXiv:2101.02579 [nucl-ex]

    work page arXiv 2021

  63. [63]

    Acharyaet al.(ALICE), JHEP05, 085, arXiv:2002.00633 [nucl-ex]

    S. Acharyaet al.(ALICE), JHEP05, 085, arXiv:2002.00633 [nucl-ex]

    work page arXiv 2002

  64. [64]

    J. E. Parkkila, A. Onnerstad, and D. J. Kim, Phys. Rev. C104, 054904 (2021), arXiv:2106.05019 [hep-ph]

    work page arXiv 2021

  65. [65]

    Hamby, Environ Monit Assess32, 135 (1994)

    D. Hamby, Environ Monit Assess32, 135 (1994)

    1994

  66. [66]

    Adamet al.(ALICE), Phys

    J. Adamet al.(ALICE), Phys. Rev. C94, 034903 (2016), arXiv:1603.04775 [nucl-ex]

    work page arXiv 2016

  67. [67]

    B. H. Alver, C. Gombeaud, M. Luzum, and J.-Y. Olli- trault, Phys. Rev. C82, 034913 (2010), arXiv:1007.5469 [nucl-th]

    work page arXiv 2010

  68. [68]

    Aamodt et al

    K. Aamodtet al.(ALICE), Phys. Rev. Lett.105, 252302 (2010), arXiv:1011.3914 [nucl-ex]

    work page arXiv 2010

  69. [69]

    Di Francesco, M

    P. Di Francesco, M. Guilbaud, M. Luzum, and J.-Y. Ollitrault, Phys. Rev. C95, 044911 (2017), arXiv:1612.05634 [nucl-th]

    work page arXiv 2017

  70. [70]

    J. Jia, J. Phys. G41, 124003 (2014), arXiv:1407.6057 [nucl-ex]

    work page arXiv 2014

  71. [71]

    F. G. Gardim and J.-Y. Ollitrault, Phys. Rev. C103, 044907 (2021), arXiv:2010.11919 [nucl-th]

    work page arXiv 2021

  72. [72]

    Adamet al.(ALICE), Anisotropic flow of charged par- ticles in Pb-Pb collisions at√sNN = 5.02 TeV, Phys

    J. Adamet al.(ALICE), Phys. Rev. Lett.116, 132302 (2016), arXiv:1602.01119 [nucl-ex]

    work page arXiv 2016

  73. [73]

    Adamet al.(ALICE), Phys

    J. Adamet al.(ALICE), Phys. Rev. Lett.116, 222302 (2016), arXiv:1512.06104 [nucl-ex]

    work page arXiv 2016

  74. [74]

    B. B. Abelevet al.(ALICE), Eur. Phys. J. C74, 3077 (2014), arXiv:1407.5530 [nucl-ex]

    work page arXiv 2014

  75. [75]

    Abelevet al.(ALICE), Phys

    B. Abelevet al.(ALICE), Phys. Rev. C88, 044910 (2013), arXiv:1303.0737 [hep-ex]

    work page arXiv 2013

  76. [76]

    Aamodt et al

    K. Aamodtet al.(ALICE), Phys. Rev. Lett.106, 032301 (2011), arXiv:1012.1657 [nucl-ex]

    work page arXiv 2011

  77. [77]

    Pratt and G

    S. Pratt and G. Torrieri, Phys. Rev. C82, 044901 (2010), arXiv:1003.0413 [nucl-th]

    work page arXiv 2010

  78. [78]

    Acharyaet al.(ALICE), Phys

    S. Acharyaet al.(ALICE), Phys. Rev. C97, 024906 (2018), arXiv:1709.01127 [nucl-ex]

    work page arXiv 2018

  79. [79]

    S. A. Basset al., Prog. Part. Nucl. Phys.41, 255 (1998), arXiv:nucl-th/9803035

    work page arXiv 1998

  80. [80]

    Bleicheret al., Relativistic hadron hadron collisions in the ultrarelativistic quantum molecular dynamics model, J

    M. Bleicheret al., J. Phys. G25, 1859 (1999), arXiv:hep-ph/9909407

    work page arXiv 1999

Showing first 80 references.