Pith. sign in

REVIEW 3 major objections 4 minor 47 references

Path-length dependence of parton energy loss across collision systems: a Bayesian analysis of charged-particle RAA, consistent with a universal exponent from O+O to Pb+Pb

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that the path-length exponent of parton energy loss is 1.78, near the radiative value 2, across systems from oxygen to lead.

desk verdict A well-measured effective exponent with an overreaching mechanism claim: the 'excludes collisional' conclusion ignores the density–path-length degeneracy the paper itself quantifies. read the letter →

arxiv 2607.25727 v1 pith:NKZCXFVH submitted 2026-07-28 nucl-th hep-ph

Fouad A. Majeed , Hussein Ali Hussein Al Naffakh , Sarah M. Obaid , Muntaha Abdullah Reishaan This is my paper · ORCID
classification nucl-thhep-ph
keywords quark-gluonplasmajetquenchingpartonenergylossnuclearmodificationfactorpath-lengthdependencelight-ioncollisionsBayesianinferencesystem-sizescan
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

What the paper tries to establish: the way parton energy loss grows with in-medium path length $L$ can be read off the collision-system size itself, using the new light-ion collision data. Fitting charged-particle suppression $R_{AA}$ across O+O, Ne+Ne, Xe+Xe and Pb+Pb with a minimal probabilistic forward model yields an effective exponent $n_{\rm eff}=1.78\pm0.15\pm0.05$, close to the radiative prediction $\Delta E\propto L^2$, decisively disfavouring the linear collisional and cubic strong-coupling scalings. If true, this would mean one mechanism—medium-induced radiation—governs jet quenching from the smallest to the largest quark-gluon plasma droplets, with no regime change between them. It would also place a lower bound on the microscopic path-length exponent, excluding purely collisional energy loss.

What carries the argument

The carrying object is a three-parameter forward model, $R_{AA}(p_T)=[p_T/(p_T+\Delta p_T)]^{a(p_T)}$ with fractional energy loss $\Delta p_T=\kappa\,\rho\,G^n\,p_T^\beta$, where $G$ is a dimensionless geometry ratio (mean participant number to the one-third power, normalized to the heaviest system), $\rho$ the relative medium density, $a(p_T)$ a measured spectral index from proton-proton data, and $n$ the exponent of interest. The extraction uses a correlated-Gaussian likelihood, a Monte-Carlo nuclear geometry model for $G$, and nested-sampling evidence for model comparison. The step from the effective to the microscopic exponent is carried by a closure study in which pseudo-data are generated with a fixed microscopic exponent and a fluctuating energy loss; because fluctuations only lower the recovered effective exponent, the measurement bounds the microscopic exponent from below. The other load-bearing identity is the size-scan degeneracy: across systems the density and the path length both grow roughly as the cube root of the participant number, so the data constrain only the combination $n_{\rm eff}\approx 1+n_{\rm pure}$, and the paper quotes $n_{\rm eff}$ as its primary result.

What would settle it

Run a purely collisional energy-loss calculation on the same four collision geometries with realistic density growth from oxygen to lead; if it reproduces the measured suppression with an effective exponent of at least 1.78, the claim that purely collisional loss is excluded is wrong.

Watch

Extended reading notes

Core claim

At fixed collision geometry, the paper argues, fluctuations of the energy loss can only pull the effective system-size exponent below its microscopic counterpart. Combined with the measured $n_{\rm eff}=1.78$, this gives $n_{\rm micro}\ge 1.78$ and excludes $n_{\rm micro}=1$, i.e. purely collisional energy loss, for any fluctuation width. The same forward model finds probabilistic evidence for non-zero energy loss already in oxygen-oxygen collisions alone, and a model-comparison test finds no statistical preference for a separate exponent in small versus large systems, consistent with one universal radiative-dominated regime from $A=16$ to $A=208$.

Load-bearing premise

The argument that rules out purely collisional energy loss assumes that the fixed-density closure calculation transfers to the real system-size scan, where a denser medium accompanies every longer path; under that real-world correlation, collisional loss would look almost as steep as the measured value.

Editorial extensions

If this is right

  • If the central claim is correct, future argon and krypton minimum-bias measurements should give $R_{AA}(10\,\mathrm{GeV})=0.55\pm0.03$ and $0.41\pm0.03$, a direct experimental test of the universal exponent.
  • A single effective exponent across the four systems implies no change of energy-loss regime from the smallest to the largest system, so the light-ion data become a clean mechanism lever that avoids centrality-selection biases.
  • The lower bound on the microscopic exponent would rule out any purely collisional model, regardless of fluctuation width, as long as the fixed-density mapping holds.
  • The energy-loss magnitude maps to an effective transport coefficient $\hat q/T^3\approx2\text{–}5$, tying the geometric exponent to an independent transport scale.

Reading between the lines

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

  • A testable extension the authors leave implicit: combining the minimum-bias size scan with centrality-differential or azimuthal-anisotropy measurements inside one system would vary path length at roughly fixed density and break the density-path-length degeneracy that the cross-system scan cannot.
  • If future argon or krypton data fall outside the predicted bands, the deviation could mean either a non-universal exponent or an incorrect density-growth model; measuring the same systems in centrality classes would separate the two.
  • The fluctuation-direction assumption (fluctuations only lower the effective exponent) may not hold in an expanding hydrodynamic medium; a transport-level check of that direction would decide whether the lower bound on the microscopic exponent survives.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 manuscript presents a Bayesian extraction of the system-size dependence of charged-particle R_AA in O+O, Ne+Ne, Xe+Xe, and Pb+Pb. A forward model with a data-driven spectral-index baseline and Monte-Carlo Glauber geometry yields an effective system-size exponent n_eff = 1.78 +/- 0.15 (stat) +/- 0.05 (syst). The authors report decisive Bayes factors against fixed effective exponents n=1 and n=3, interpret the result as radiative dominance, and use a fixed-density fluctuation closure to argue that purely collisional energy loss is excluded. They also report a universality test across systems, Bayesian evidence for non-zero suppression in O+O alone, a systematic budget, a 160-variant sensitivity scan, normalizing-flow and Gaussian-process cross-checks, and falsifiable predictions for Ar+Ar and Kr+Kr.

Significance. The statistical infrastructure is a genuine strength: the covariance propagation, coverage tests, nested-sampling evidence computation, 160-variant robustness scan, reproducible code, and concrete predictions for unmeasured systems are valuable and go beyond many phenomenological fits. If interpreted strictly as a geometry-level benchmark, the effective exponent is a useful quantity. However, the central mechanism claim is not supported. The comparison of n_eff with fixed-density microscopic exponents n=1,2,3 ignores the density growth across system size that the paper itself documents in Sec. 4.4. The advertised exclusion of collisional energy loss is therefore not established by the analysis as presented.

major comments (3)
  1. [4.2 / Table 5] The model-selection evidence ratios in Table 5 compare fixed-rho=1 models with Delta E proportional to G^n (n=1,2,3) against the data, and the abstract uses these ratios to claim a decisive exclusion of collisional energy loss. This is a category mismatch: the theoretical exponents in Table 5 are microscopic path-length exponents at fixed medium density, while n_eff is the growth of suppression with the geometry ratio G with rho set to unity. Under the paper's own Eq. (4) and its Monte-Carlo geometry with p approximately 1.1, a purely collisional microscopic exponent n_pure=1 predicts n_eff approximately 2.1, which is within about 2 sigma of the measured 1.78 +/- 0.15 and inside the evidence-peak range n approximately 1.8 +/- 0.3 of Fig. 3. The values 2 Delta ln Z = -29 and -48 reject n_eff=1 and n_eff=3; they do not reject a collisional mechanism once density growth is included. The abstract's claim that the analysis excludes purely collisional energy loss does not follow.
  2. [4.2.1] The fluctuation closure study is performed at fixed density, with pseudo-data generated at rho=1 while G is varied, and it demonstrates that fluctuations lower the recovered n_eff relative to n_micro. The text then applies the resulting bound n_micro >= n_eff to the actual system-size scan. This application is not valid: in the real scan the medium density grows with system size, and the section itself concedes that density evolution acts in the opposite sense to fluctuations. The bound n_micro >= n_eff is an artifact of holding rho fixed and does not constrain the microscopic exponent for the real data. Supporting the exclusion claim would require a closure study in which both rho and G vary according to the Glauber geometry, or an explicit model of the density-length correlation.
  3. [4.4 / Table 4] There is an internal inconsistency between the radiative-favoring conclusion and the paper's own density decomposition. In Sec. 4.4, Eq. (4) gives n_eff = p + n_pure, and Table 4 reports n_pure approximately 0.64. With p approximately 1.1, a radiative microscopic exponent n_pure=2 would correspond to n_eff approximately 3.1, which the same data reject with 2 Delta ln Z approximately -48. The manuscript cannot simultaneously use Eq. (4) to argue that n_pure is not a clean path-length exponent and use n_eff to claim radiative dominance without addressing this conflict. The microscopic hypotheses should be tested with density growth included, or the paper should explicitly state that the data do not discriminate microscopic mechanisms.
minor comments (4)
  1. [4.2.1] The phrase 'The referee's concern is thus well founded in principle' appears to be an artifact of an earlier review round and should be removed or rewritten in the impersonal style of the rest of the paper.
  2. [Table 6] The headline systematic of +/-0.05 excludes the forward-model-form shift delta n = +0.18 and the geometry-proxy spread [1.71, 1.82]; since these are reported in the same table, the abstract should either quote a combined systematic or state explicitly which contributions are included in the quoted uncertainty.
  3. [3.2 / 4.6] The low chi-squared per degree of freedom of about 0.4 and the posterior-predictive p-value near 1.0 are unusual and indicate that the published systematic uncertainties are treated very conservatively; the text should more prominently state this implication and discuss whether the quoted parameter uncertainties are correspondingly over-covered.
  4. [Figure 5] The relation between the log-normal width parameter sigma_w and the quoted relative width sigma(Delta E)/<Delta E> = 0.81 is not defined; please provide the conversion used in the text.

Circularity Check

2 steps flagged · score 6.0 of 10

The 'exclusion of purely collisional energy loss' compares n_eff (defined with ρ≡1) to fixed-density exponents n=1,2,3, while Sec. 4.4's Eq. (4) gives n_eff=1+n_pure; the mechanism claim reduces to the closure's definitional mapping.

  1. self definitional [Sec. 4.2.1, pseudo-data closure ("From the effective to the microscopic exponent"); Eq. (1)]
    "Pseudo-data are generated with the quenching-weight form of Sec. 4.7, in which the energy loss is drawn from a log-normal distribution P(∆E) of relative width σw at a fixed microscopic exponent nmicro. The mean of the distribution is held exactly at ⟨∆E⟩∝G^n for every σw... At σw = 0 the procedure returns neff = nmicro to three decimal places for all three scenarios."

    The closure defines the 'microscopic exponent' as the exponent n in the same mean scaling ⟨∆E⟩∝G^n that Eq. (1) fits, and the pseudo-data are generated with the same parametrization later used to extract n_eff. At zero width the equality n_eff=n_micro is therefore an identity of the generator, not an empirical mapping. The subsequent bound 'n_micro ≥ n_eff = 1.78' is a property of this fixed-density (ρ=1) construction; it does not by itself constrain the microscopic exponent of a real system-size scan in which the density grows with G.

  2. self definitional [Abstract and Conclusions ('excluding purely collisional energy loss'); Sec. 4.2.1 bound; Sec. 4.4 Eq. (4)]
    "with the participant scalings L∼R∝⟨Npart⟩1/3 and S∝R2 ∝⟨Npart⟩2/3, the density is ρ∝⟨Npart⟩/S∝⟨Npart⟩1/3—it grows with system size identically to L. The energy loss then scales as ∆E∝ρL^npure ∝⟨Npart⟩(1+npure)/3, whereas the effective (geometry-only) parametrisation gives ∆E∝⟨Npart⟩neff/3... Since fluctuations can only reduce the effective exponent, and the collision geometry is held fixed, the measurement constrains the microscopic exponent from below: nmicro ≥ neff = 1.78±0.15."

    Under the paper's own Eq. (4), the effective exponent is n_eff = 1 + n_pure (p≈1.14 in the MC geometry), so a collisional microscopic exponent n_pure=1 predicts n_eff≈2.1, compatible with the measured 1.78±0.15. The abstract's 'excluding purely collisional energy loss' is obtained by comparing n_eff to the fixed-density value n=1, i.e. by treating the microscopic and effective exponents as the same quantity. That identification is imposed by the ρ≡1 definition of n_eff in Eq. (1) and by the fixed-geometry closure of Sec. 4.2.1, not derived from the system-size data; the exclusion is therefore equivalent to the defining assumption.

full rationale

The headline number n_eff=1.78±0.15 is a genuine fit to external CMS/HEPData R_AA values and is not circular; the Bayesian machinery, covariance model, dual-Glauber geometry, coverage tests, and the LOSO/GP cross-checks are self-contained and are validated against external data. The circularity enters at the interpretation layer where the paper converts n_eff into a statement about the microscopic mechanism. In Sec. 4.2.1 the 'microscopic exponent' is inserted into pseudo-data as the exponent of the very same G^n mean scaling that Eq. (1) fits; the recovered n_eff=n_micro at zero width is a definitional identity, and the bound n_micro≥n_eff holds only in that fixed-density construction. Section 4.4 then provides Eq. (4), which states n_eff=1+n_pure for the real scan because the density grows like L. Thus a collisional mechanism (n_pure=1) predicts n_eff≈2.1, which the measured 1.78±0.15 does not exclude; the claimed exclusion of purely collisional energy loss is an artifact of comparing the ρ≡1 effective exponent with fixed-density exponents n=1,2,3. The paper itself flags the degeneracy ('only the combination n_eff=1+n_pure is constrained') and the fixed-geometry limitation of the closure, but the abstract and conclusions still assert 'excluding purely collisional energy loss'. No load-bearing self-citation is present; the cited transport literature is external. Score 6 reflects that the central mechanism claim reduces by construction to the defining fixed-density mapping, while the primary effective-exponent extraction retains independent empirical content.

Assumptions & free parameters 5 free parameters · 8 assumptions · 0 invented entities

The central extraction depends on three fitted parameters (kappa, n, beta), one hand-chosen correlation length (xi), and several domain assumptions about how RAA maps to energy loss, how the spectral index scales, how the medium density varies with system size and collision energy, and how minimum-bias Glauber geometry represents the medium. No invented entities are introduced.

free parameters (5)
  • kappa (overall energy-loss scale) = 1.14 (+0.19/-0.16)
    Fitted in Eq. (1) to the four-system RAA dataset; sets the absolute magnitude of the fractional energy loss.
  • n (effective system-size exponent) = 1.78 +/- 0.15 (stat)
    Primary fitted parameter; the exponent of the geometry ratio G in the forward model with rho = 1.
  • beta (pT dependence of fractional loss) = 0.335 (+0.058/-0.059)
    Fitted simultaneously with kappa and n; controls the mild pT dependence of Delta-pT.
  • xi (covariance correlation length) = 4 bins
    Chosen by hand rather than fitted. The authors report n is stable for xi in {2,4,6,8} and chi2/dof rises; they adopt the conservative value giving chi2/dof < 1. This choice affects the quoted uncertainties and evidence values.
  • sigma_w (fluctuation width of P(Delta-E) in closure) = 0.71 (for the log-normal form)
    In the Sec. 4.2.1 inversion, sigma_w is adjusted so a microscopic exponent of 2 reproduces the measured n_eff = 1.78. It is effectively fitted to the data within the closure and then used to argue the required width is typical.
assumptions (8)
  • domain assumption Multiplicative fractional energy loss maps to RAA through the local spectral index a(pT) via Eq. (1).
    Standard approximation for steeply falling spectra, assumed without derivation from a full transport calculation.
  • domain assumption The spectral index for the higher-energy systems is obtained by x_T scaling of the 5.02 TeV pp spectrum.
    Approximate kinematic rescaling; the authors note residual NLO PDF/FF effects are small (delta n < 0.005).
  • domain assumption The medium density grows with collision energy as (sqrt(s_NN)/5.02)^0.31, from the ALICE charged-multiplicity power law.
    Exponent 0.31 taken from prior ALICE measurements over 0.9 to 5.44 TeV, extrapolated here to the medium density.
  • domain assumption Minimum-bias Glauber geometry with a static medium represents the path-length and density ratios.
    The medium is not evolved hydrodynamically; the authors acknowledge a diluting medium would weight long paths differently (Sec. 5 Limitations).
  • ad hoc to paper The participant areal density rho proportional to <Npart>/S describes the relative density.
    This specific density model enters the 'pure' exponent n_pure; Sec. 4.4 shows the density-path-length split is degenerate and n_pure is model-dependent.
  • domain assumption Energy-loss fluctuations can only lower the effective exponent below the microscopic one.
    Demonstrated in the paper's closure for log-normal, gamma, and BDMPS-like distributions, but used as a general bound and applied to a fit that includes density growth.
  • domain assumption Xe+Xe 0-80% centrality approximates minimum bias.
    CMS Xe+Xe RAA is reported for 0-80% rather than true MB; the authors estimate the resulting bias in n is <= 0.05.
  • ad hoc to paper The covariance is block-diagonal with no correlations between collision systems and an exponential bin-to-bin correlation with xi = 4.
    Assumed correlation model; the value xi = 4 is chosen by the authors, not derived from data.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Path-length dependence of parton energy loss across collision systems: a Bayesian analysis of charged-particle RAA, consistent with a universal exponent from O+O to Pb+Pb." pith.science (2026). https://pith.science/paper/NKZCXFVH

@misc{pith2026260725727,
  author       = {Pith},
  title        = {Pith review of: Path-length dependence of parton energy loss across collision systems: a Bayesian analysis of charged-particle RAA, consistent with a universal exponent from O+O to Pb+Pb},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NKZCXFVH}},
  note         = {Machine review of arXiv:2607.25727}
}
abstract

How parton energy loss in the quark-gluon plasma (QGP) scales with the in-medium path length $L$ encodes the mechanism: collisional ($\Delta E \propto L$), radiative ($\Delta E \propto L^2$), or strong-coupling ($\Delta E \propto L^3$). Exploiting the new CERN LHC light-ion data, we extract this scaling from the system size itself, jointly analysing CMS charged-particle nuclear modification factors $R_{AA}$ in four systems - O+O, Ne+Ne, Xe+Xe and Pb+Pb - spanning mass number $A = 16$ to $208$. A Bayesian analysis with a data-driven spectral baseline and a Monte-Carlo Glauber geometry yields an effective system-size exponent $n = 1.78 \pm 0.15\,\mathrm{(stat)} \pm 0.05\,\mathrm{(syst)}$. Nested-sampling model selection decisively favours an effective exponent near the radiative value ($n = 2$) over the collisional ($n = 1$) and strong-coupling ($n = 3$) values, a conclusion stable across all 160 analysis variants. Because fluctuations can only lower the effective exponent below its microscopic counterpart, the measurement bounds the latter from below at fixed geometry, excluding purely collisional energy loss. The medium density and the path length are degenerate across system size, so we quote the effective exponent as our primary result. A Bayes-factor test finds no change of regime between small and large systems, consistent with a universal exponent; the same framework gives decisive evidence for non-zero energy loss in O+O alone, quantifying the onset of suppression in the smallest system. The energy-loss magnitude corresponds to $\hat{q}/T^3 \approx 2$--$5$, consistent with the JETSCAPE determination.

Figures

Figures reproduced from arXiv: 2607.25727 by the authors.

Figure 1
Figure 1. End-to-end analysis pipeline. 2 Data and observable We use published CMS charged-particle RAA (pT ) measure￾ments for four A+A systems, summarised in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. ). The extracted exponents for the three geometry proxies are collected in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Bayesian evidence landscape as a function of the fixed exponent. 5 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: The measured exponent on the theoretical spectrum of energy-loss mechanisms [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 7
Figure 7. Figure 7: Effective and density-normalised exponents by geometry proxy. 4.5 Universality across system size We test whether a single exponent describes the entire range by comparing a universal model (one n for all systems) to a broken model with separate exponents for the light…
Figure 6
Figure 6. Figure 6: Bayesian evidence for parton energy loss in O+O alone. 4.4 Density–path-length degeneracy It is tempting to factor the effective exponent into a “pure” path-length exponent and a density contribution by writ￾ing ∆E ∝ ρ L npure with ρ ∝ ⟨Npart⟩/S. Doing so yields a sub-…
Figure 8
Figure 8. Figure 8: Left: universality test. Right: Glauber geometry cross-check. bracketing the nominal values throughout. The Laplace and full-MCMC posteriors coincide here (the posterior is near-Gaussian in the fitted parameters), so this also vali￾dates the Gaussian approximation used…
Figure 9
Figure 9. Figure 9: Systematic budget for the reference exponent. 4.8 Sensitivity analysis To assess how strongly the extraction depends on the anal￾ysis choices—a key robustness question for any Bayesian determination—we repeat the fit and the model selection over a grid spanning all maj…
Figure 10
Figure 10. Figure 10: Sensitivity over geometry proxy and covariance length. Left: exponent. Middle, right: evidence ratios [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: The Gaussian process as a calibrated prediction engine. Left: held-out closure. Right: future systems. 4.10 Quantitative comparison with JETSCAPE The radiative interpretation can be checked against an in￾dependent, state-of-the-art extraction of the jet transport coef…
Figure 13
Figure 13. Figure 13: Consistency with the JETSCAPE jet-transport extraction. 4.11 System-by-system stability and predic￾tions for future systems Leave-one-system-out stability. To verify that the result is not driven by any single system, we repeat the extraction four times, each time rem…
Figure 12
Figure 12. Figure 12: Neural posterior estimation against the Markov chain. The simulation-based-calibration check is reported in Sec. 3.5. pected) but does not bias the central value. No single system drives the radiative conclusion [PITH_FULL_IMAGE:figures/full_fig_p011_12.png]
Figure 14
Figure 14. Figure 14: Left: leave-one-system-out stability. Right: predictions for Ar+Ar and Kr+Kr. pT = 10 GeV for O+O; the CMS measurement gives a mini￾mum RAA = 0.69±0.04, consistent with this range. System￾size scans of identified particles had likewise been advo￾cated as discriminatin…
Figure 15
Figure 15. Figure 15: External cross-experiment check against ALICE Xe+Xe. 6 Conclusions We have presented a calibrated Bayesian and simulation￾based-inference extraction of the path-length dependence of parton energy loss from the system-size systematics of charged-particle RAA, using rea…

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

47 extracted references · 13 canonical work pages

  1. [1]

    Gyulassy and L

    M. Gyulassy and L. McLerran, Nucl. Phys. A750, 30 (2005)

    2005

  2. [2]

    U. A. Wiedemann, inRelativistic Heavy Ion Physics, Landolt-Börnstein23, 521 (2010), arXiv:0908.2306

    arXiv 2010

  3. [3]

    Baier, Y

    R. Baier, Y . L. Dokshitzer, A. H. Mueller, S. Peigné, and D. Schiff, Nucl. Phys. B483, 291 (1997)

    work page 1997

  4. [4]

    Gyulassy, P

    M. Gyulassy, P. Lévai, and I. Vitev, Nucl. Phys. B 594, 371 (2001)

    work page 2001

  5. [5]

    Casalderrey-Solana, H

    J. Casalderrey-Solana, H. Liu, D. Mateos, K. Ra- jagopal, and U. A. Wiedemann,Gauge/String Duality, Hot QCD and Heavy Ion Collisions(Cambridge University Press, 2014)

    work page 2014

  6. [6]

    Observation of Suppressed Charged-Particle Production in Ultrarelativistic Oxygen- Oxygen Collisions,

    CMS Collaboration, “Observation of Suppressed Charged-Particle Production in Ultrarelativistic Oxygen- Oxygen Collisions,” CMS-HIN-25-008, arXiv:2510.09864 (2025)

    arXiv 2025

  7. [7]

    System-size dependence of charged-particle suppression in nucleus-nucleus collisions,

    CMS Collaboration, “System-size dependence of charged-particle suppression in nucleus-nucleus collisions,” CMS-HIN-25-014, arXiv:2602.21325 (2026); HEPData record ins3123773

    arXiv 2026

  8. [8]

    Charged-particle nuclear mod- ification factors in XeXe collisions at √sNN =5.44 TeV ,

    CMS Collaboration, “Charged-particle nuclear mod- ification factors in XeXe collisions at √sNN =5.44 TeV ,” JHEP10, 138 (2018), arXiv:1809.00201; HEPData record ins1692558

    arXiv 2018

Show all 47 references
  1. [9]

    Charged-particle nuclear mod- ification factors in PbPb and pPb collisions at √sNN = 5.02 TeV ,

    CMS Collaboration, “Charged-particle nuclear mod- ification factors in PbPb and pPb collisions at √sNN = 5.02 TeV ,” JHEP04, 039 (2017), arXiv:1611.01664; HEP- Data record ins1496050

    2017 arXiv

  2. [10]

    Transverse momentum spectra and nuclear modification factors of charged parti- cles in Xe-Xe collisions at√sNN =5.44 TeV ,

    ALICE Collaboration, “Transverse momentum spectra and nuclear modification factors of charged parti- cles in Xe-Xe collisions at√sNN =5.44 TeV ,” Phys. Lett. B 788, 166 (2019), arXiv:1805.04399

    2019 arXiv

  3. [11]

    Dis- covering Partonic Rescattering in Light Nucleus Collisions,

    A. Huss, A. Kurkela, A. Mazeliauskas, R. Paate- lainen, W. van der Schee, and U. A. Wiedemann, “Dis- covering Partonic Rescattering in Light Nucleus Collisions,” Phys. Rev. Lett.126, 192301 (2021), arXiv:2007.13754

    2021 arXiv

  4. [12]

    Predicting parton energy loss in small collision systems,

    A. Huss, A. Kurkela, A. Mazeliauskas, R. Paate- lainen, W. van der Schee, and U. A. Wiedemann, “Predicting parton energy loss in small collision systems,” Phys. Rev. C 103, 054903 (2021), arXiv:2007.13758

    2021 arXiv

  5. [13]

    System-size scan ofDmesonR AA andv n using PbPb, XeXe, ArAr, and OO collisions at the LHC,

    R. Katz, C. A. G. Prado, J. Noronha-Hostler, and A. A. P. Suaide, “System-size scan ofDmesonR AA andv n using PbPb, XeXe, ArAr, and OO collisions at the LHC,” Phys. Rev. C102, 041901 (2020), arXiv:1907.03308

    2020 arXiv

  6. [14]

    Absence of jet quench- ing in peripheral nucleus-nucleus collisions,

    C. Loizides and A. Morsch, “Absence of jet quench- ing in peripheral nucleus-nucleus collisions,” Phys. Lett. B 773, 408 (2017), arXiv:1705.08856

    2017 arXiv

  7. [15]

    De- termining the jet transport coefficient bqfrom inclusive hadron suppression measurements using Bayesian pa- rameter estimation,

    JETSCAPE Collaboration (S. Caoet al.), “De- termining the jet transport coefficient bqfrom inclusive hadron suppression measurements using Bayesian pa- rameter estimation,” Phys. Rev. C104, 024905 (2021), arXiv:2102.11337

    2021 arXiv

  8. [16]

    Bayesian analysis of QGP jet transport using multi-scale modeling,

    JETSCAPE Collaboration (R. Ehlerset al.), “Bayesian analysis of QGP jet transport using multi-scale modeling,” arXiv:2208.07950 (2022)

    2022 arXiv

  9. [17]

    Bayesian estimation of the specific shear and bulk viscosity of the quark-gluon plasma,

    J. E. Bernhard, J. S. Moreland, and S. A. Bass, “Bayesian estimation of the specific shear and bulk viscosity of the quark-gluon plasma,” Nature Phys.15, 1113 (2019)

    2019

  10. [18]

    Glauber Modeling in High-Energy Nuclear Collisions,

    M. L. Miller, K. Reygers, S. J. Sanders, and P. Steinberg, “Glauber Modeling in High-Energy Nuclear Collisions,” Ann. Rev. Nucl. Part. Sci.57, 205 (2007), arXiv:nucl-ex/0701025

    2007 arXiv

  11. [19]

    Progress in the Glauber Model at Collider Energies,

    D. d’Enterria and C. Loizides, “Progress in the Glauber Model at Collider Energies,” Ann. Rev. Nucl. Part. Sci.71, 315 (2021), arXiv:2011.14909

    2021 arXiv

  12. [20]

    Improved version of the PHOBOS Glauber Monte Carlo,

    C. Loizides, J. Nagle, and P. Steinberg, “Improved version of the PHOBOS Glauber Monte Carlo,” SoftwareX 1-2, 13 (2015), arXiv:1408.2549

    2015 arXiv

  13. [21]

    Centrality and pseudorapid- ity dependence of the charged-particle multiplicity density in Xe–Xe collisions at √ sNN = 5.44 TeV ,

    ALICE Collaboration, “Centrality and pseudorapid- ity dependence of the charged-particle multiplicity density in Xe–Xe collisions at √ sNN = 5.44 TeV ,” Phys. Lett. B 790, 35 (2019)

    2019

  14. [22]

    Centrality and √sNN de- pendence of charged-particle multiplicity at the LHC,

    ALICE Collaboration, “Centrality and √sNN de- pendence of charged-particle multiplicity at the LHC,” Phys. Rev. Lett.116, 222302 (2016), arXiv:1512.06104

    2016 arXiv

  15. [23]

    Review of Particle Physics,

    Particle Data Group, R. L. Workmanet al., “Review of Particle Physics,” Prog. Theor. Exp. Phys.2022, 083C01 (2022)

    2022

  16. [24]

    The frontier of simulation-based inference,

    K. Cranmer, J. Brehmer, and G. Louppe, “The frontier of simulation-based inference,” Proc. Natl. Acad. Sci.117, 30055 (2020), arXiv:1911.01429

    2020 arXiv

  17. [25]

    Simulation-based inference in particle physics,

    J. Brehmer, “Simulation-based inference in particle physics,” Nature Rev. Phys.3, 305 (2021)

    2021

  18. [26]

    Normaliz- ing Flows for Probabilistic Modeling and Inference,

    G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normaliz- ing Flows for Probabilistic Modeling and Inference,” J. Mach. Learn. Res.22, 1 (2021), arXiv:1912.02762. 16

    2021 arXiv

  19. [27]

    Neural Spline Flows,

    C. Durkan, A. Bekasov, I. Murray, and G. Papa- makarios, “Neural Spline Flows,” Adv. Neural Inf. Pro- cess. Syst.32(2019), arXiv:1906.04032

    2019 arXiv

  20. [28]

    Validating Bayesian Inference Algorithms with Simulation-Based Calibration,

    S. Talts, M. Betancourt, D. Simpson, A. Vehtari, and A. Gelman, “Validating Bayesian Inference Algorithms with Simulation-Based Calibration,” arXiv:1804.06788 (2018)

    2018 arXiv

  21. [29]

    C. E. Rasmussen and C. K. I. Williams,Gaussian Processes for Machine Learning(MIT Press, 2006)

    2006

  22. [30]

    NGBoost: Natural Gradi- ent Boosting for Probabilistic Prediction,

    T. Duanet al., “NGBoost: Natural Gradi- ent Boosting for Probabilistic Prediction,” Proc. 37th Int. Conf. Mach. Learn. (2020), arXiv:1910.03225

    2020 arXiv

  23. [31]

    emcee: The MCMC Hammer,

    D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Goodman, “emcee: The MCMC Hammer,” Publ. As- tron. Soc. Pac.125, 306 (2013), arXiv:1202.3665

    2013 arXiv

  24. [32]

    dynesty: a dynamic nested sam- pling package for estimating Bayesian posteriors and ev- idences,

    J. S. Speagle, “dynesty: a dynamic nested sam- pling package for estimating Bayesian posteriors and ev- idences,” Mon. Not. R. Astron. Soc.493, 3132 (2020), arXiv:1904.02180

    2020 arXiv

  25. [33]

    Nested sampling for general Bayesian computation,

    J. Skilling, “Nested sampling for general Bayesian computation,” Bayesian Anal.1, 833 (2006)

    2006

  26. [34]

    Zuko: Normalizing Flows in PyTorch,

    F. Rozetet al., “Zuko: Normalizing Flows in PyTorch,” Zenodo (2022),https://github.com/ probabilists/zuko

    2022

  27. [35]

    Quenching of hadron spectra in media,

    R. Baier, Y . L. Dokshitzer, A. H. Mueller, and D. Schiff, “Quenching of hadron spectra in media,” JHEP 09, 033 (2001), arXiv:hep-ph/0106347

    2001 arXiv

  28. [36]

    Bayes factors,

    R. E. Kass and A. E. Raftery, “Bayes factors,” J. Am. Stat. Assoc.90, 773 (1995)

    1995

  29. [37]

    Jeffreys,Theory of Probability, 3rd ed

    H. Jeffreys,Theory of Probability, 3rd ed. (Oxford University Press, 1961)

    1961

  30. [38]

    Flavor and path-length dependence of jet quenching from inclusive jet andγ-jet suppression,

    A. Ogrodnik, M. Rybá ˇr, and M. Spousta, “Flavor and path-length dependence of jet quenching from inclusive jet andγ-jet suppression,” Eur. Phys. J. C85, 899 (2025), arXiv:2407.11234

    2025

  31. [39]

    Constraints on the path- length dependence of jet quenching in nuclear collisions at RHIC and LHC,

    B. Betz and M. Gyulassy, “Constraints on the path- length dependence of jet quenching in nuclear collisions at RHIC and LHC,” JHEP08, 090 (2014) [Erratum: JHEP 10, 043 (2014)], arXiv:1404.6378

    2014 arXiv

  32. [40]

    Constraining the physics of jet quench- ing,

    T. Renk, “Constraining the physics of jet quench- ing,” Phys. Rev. C85, 044903 (2012), arXiv:1112.2503

    2012 arXiv

  33. [41]

    Probing the path-length dependence of parton energy loss via scaling properties in heavy ion collisions,

    F. Arleo and G. Falmagne, “Probing the path-length dependence of parton energy loss via scaling properties in heavy ion collisions,” Phys. Rev. D109, L051503 (2024), arXiv:2212.01324

    2024 arXiv

  34. [42]

    Bayesian inference of the path-length dependence of jet energy loss,

    J. Wu, W. Ke, and X.-N. Wang, “Bayesian inference of the path-length dependence of jet energy loss,” Phys. Rev. C108, 034911 (2023), arXiv:2304.06339

    2023 arXiv

  35. [43]

    Medium-induced QCD cascade: democratic branching and wave turbulence,

    J.-P. Blaizot, E. Iancu, and Y . Mehtar-Tani, “Medium-induced QCD cascade: democratic branching and wave turbulence,” Phys. Rev. Lett.111, 052001 (2013), arXiv:1301.6102

    2013 arXiv

  36. [44]

    Vacuum-like jet fragmentation in a dense QCD medium,

    P. Caucal, E. Iancu, A. H. Mueller, and G. Soyez, “Vacuum-like jet fragmentation in a dense QCD medium,” Phys. Rev. Lett.120, 232001 (2018), arXiv:1801.09703

    2018 arXiv

  37. [45]

    Jet suppression and azimuthal anisotropy from RHIC to LHC,

    Y . Mehtar-Tani, D. Pablos, and K. Tywoniuk, “Jet suppression and azimuthal anisotropy from RHIC to LHC,” Phys. Rev. D110, 014009 (2024), arXiv:2402.07869

    2024 arXiv

  38. [46]

    Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements,

    R. Ehlerset al., “Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements,” Phys. Rev. C111, 054913 (2025), arXiv:2408.08247

    2025

  39. [47]

    A hybrid strong/weak cou- pling approach to jet quenching,

    J. Casalderrey-Solana, D. C. Gulhan, J. G. Milhano, D. Pablos, and K. Rajagopal, “A hybrid strong/weak cou- pling approach to jet quenching,” JHEP10, 019 (2014) [Er- ratum: JHEP 09, 175 (2015)], arXiv:1405.3864. 17

    2014 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.