Pith. sign in

REVIEW 3 major objections 6 minor 3 cited by

Constraining Jet Quenching in Heavy-Ion Collisions with Bayesian Inference

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read This paper claims that the same energy-loss distribution describes jet suppression in inclusive and photon-tagged heavy-ion data, and that gluon jets lose roughly 3.5 times more energy than quark jets — faster than simple color-charge…

desk verdict Careful, honest Bayesian analysis; the universality test is new and passes, but the super-Casimir color ratio rests on a prior that contradicts the paper's own resolved-emitter estimate. read the letter →

arxiv 2411.14552 v2 pith:NMUICDS5 submitted 2024-11-21 hep-ph hep-exnucl-th

Alexandre Falcão , Konrad Tywoniuk This is my paper · ORCID
classification hep-phhep-exnucl-th PACS 12.38.Mh25.75.-q24.85.+p
keywords jetquenchingquark-gluonplasmaenergylossdistributionBayesianinferencecolorfactorCasimirscalingnuclearmodificationphoton-taggedjets
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

Jet suppression in heavy-ion collisions is usually modeled with Monte Carlo generators that mix many assumptions; this paper asks what the data themselves say about the energy-loss mechanism. It tests whether a single flavor-dependent probability distribution — the 'quenching weight' for the energy a jet loses to the quark-gluon plasma — can describe both inclusive jet suppression and photon-tagged jet yields from the LHC, and finds those data consistent with such a universal weight. It then extracts from the same data the color dependence of energy loss, i.e., how much more energy gluon-initiated jets lose than quark-initiated ones, a quantity that distinguishes single-parton from multi-parton quenching. The paper's headline result is a color ratio $\langle \varepsilon_g \rangle / \langle \varepsilon_q \rangle \approx 3.5$, corresponding to super-Casimir scaling $(N_c/C_F)^{1+\gamma}$ with $\gamma \approx 0.5$. This matters because it suggests jets lose energy as partially developed showers of resolved subjets, not as single color charges, and it shows how much of that conclusion rests on a physically motivated prior.

What carries the argument

The central object is the flavor-dependent quenching weight $D_i(\varepsilon)$ entering the factorization $\sigma_{\rm med}(p_T) = \sum_i \int_0^\infty d\varepsilon\, D_i(\varepsilon)\, \sigma_{{\rm vac},i}(p_T+\varepsilon)$, which encodes all medium effects on the jet. The analysis wraps three two-parameter distributions (normal, log-normal, gamma), each with separate quark and gluon copies (four parameters per parametrization), in a Bayesian inference with a Gaussian-process emulator used for fast model evaluation, and checks predictions on held-out observables via posterior predictive distributions. The physical anchor that decides between otherwise degenerate fits is the mean-to-mode relation $\langle \varepsilon \rangle / \varepsilon_{\rm max} \approx 1/\alpha_s$, which converts the requirement $\alpha_s \in (0.1,0.5)$ into a bound on the tail thickness of $D_i(\varepsilon)$; when the authors restrict the prior to that bound, the three parametrizations agree on the color ratio $\mathrm{CR}$.

What would settle it

Measure an energy-loss-sensitive observable that bypasses the steeply falling spectrum bias — for example, the subleading-jet spectrum in $Z$-boson events or the low-$x_{J\gamma}$ tail of photon-jet momentum imbalance, where large energy losses are not exponentially suppressed. If such data, analyzed with the same three parametrizations and no mean-to-mode prior, give a color ratio consistent with Casimir scaling ($\mathrm{CR} \approx 2.25$) for all three distribution shapes, the super-Casimir conclusion would be shown to rest on the prior rather than on the data.

Watch

Extended reading notes

Core claim

Under the usual factorization that heavy-ion jet spectra are a convolution of the vacuum spectrum with a flavor-dependent energy-loss distribution $D_i(\varepsilon)$, the authors use Bayesian inference over three flexible parametrizations (normal, log-normal, gamma) to extract $D_q$ and $D_g$ from LHC measurements of the inclusive jet nuclear modification factor, its rapidity-dependent ratios, and photon-tagged jet yields. They find, first, that a single set of quenching weights describes both classes of observables, establishing what they call universality of the energy-loss distribution. Second, they find that the mean energy loss of gluon-initiated jets exceeds that of quark-initiated jets by a factor that, once a physical restriction on the mean-to-mode ratio ($0.1 < \alpha_s < 0.5$) is imposed, converges to about 3.5, parametrized as $\mathrm{CR} = (N_c/C_F)^{1+\gamma}$ with $\gamma \approx 0.5$. They interpret this super-Casimir scaling as evidence that energy loss acts on the partially developed parton shower — multiple resolved subjets contribute — rather than on a single parent parton.

Load-bearing premise

The super-Casimir color ratio rests on the prior assumption that the mean energy loss is not too far from the most probable energy loss (specifically, $\langle \varepsilon \rangle / \varepsilon_{\rm max}$ between 2 and 10, i.e., $0.1 < \alpha_s < 0.5$); without that restriction, the three flexible distribution shapes used in the fit give color ratios of about 3.5, 1, and 2.25, so the data alone do not single out a value above Casimir scaling.

Editorial extensions

If this is right

  • The same quenching weight describes inclusive and photon-tagged jet measurements, so future global fits can treat energy loss as medium-dominated and largely flavor-independent before the final color dependence is applied.
  • The rapidity-dependent inclusive jet data carry most of the constraining power on the gluon quenching weight; photon-tagged jet data alone cannot pin down the gluon energy-loss parameters.
  • Without the mean-to-mode prior, the inferred color ratio $\langle \varepsilon_g \rangle / \langle \varepsilon_q \rangle$ varies strongly across parametrizations (about 3.5, 1, and 2.25), so the prior is what currently permits a stable statement about color dependence from existing data.
  • The photon-tagged jet nuclear modification factor sits in tension with the global inclusive-jet fit unless nuclear-PDF effects are treated in a particular way, indicating an inconsistency between data sets that future measurements or recalibrations should resolve.

Reading between the lines

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

  • Editorial inference: If the super-Casimir color dependence is real, the ratio $\langle \varepsilon_g \rangle / \langle \varepsilon_q \rangle$ should grow with the number of resolved subjets, i.e., with jet energy at fixed radius; repeating the same analysis in higher-$p_T$ bins once less biased data are available would test this prediction.
  • Editorial inference: The near-flat, tail-heavy shapes extracted for $D(\varepsilon)$ may encode the survival bias of steeply falling spectra as much as the underlying physics; a cleaner test would come from observables that amplify rare large energy losses, such as the far tail of photon-jet momentum imbalance distributions.
  • Editorial inference: Allowing $\alpha_s$ to vary with flavor or with jet $p_T$, or adding a fourth shape parameter to the parametrizations, could reveal whether the converged value $\mathrm{CR} \approx 3.5$ survives without a hand-set prior on the mean-to-mode ratio.
  • Editorial inference: The mean-to-mode argument ties the Poisson-like radiative spectrum to the distribution shape; if elastic energy loss or medium response contribute substantially at these kinematics, the same prior logic would need revision, plausibly changing the extracted color ratio.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper performs a Bayesian inference of the event-averaged jet energy-loss distribution (quenching weight) from ATLAS data on inclusive jet suppression, rapidity-dependent suppression ratios, and photon-tagged jet yields in 0–10% central Pb-Pb collisions at √s = 5.02 TeV. The quenching weights of quark- and gluon-initiated jets are parametrized independently by three flexible two-parameter distributions (normal, log-normal, gamma), with flavor fractions obtained from PYTHIA8, and the model is evaluated via a Gaussian-process emulator. The authors report two principal findings: (i) evidence for universality of the quark/gluon quenching weights across inclusive and photon-tagged observables, based on out-of-sample cross-predictions of Analyses A and B (Sec. VA); and (ii) an extracted color ratio CR = ⟨ε_g⟩/⟨ε_q⟩ ≈ 3.5, corresponding to super-Casimir scaling CR = (N_c/C_F)^{1+γ} with γ ≈ 0.5 (Eq. 38, Sec. VC). The color-ratio result is obtained after imposing a theory-informed prior that restricts the mean-to-mode ratio of the energy-loss distributions (Sec. VC); with flat priors the three parametrizations give inconsistent values CR ≈ 3.5, ≈ 1, and ≈ 2.25 (Fig. 11).

Significance. The methodology is the paper's main strength: the closure tests (Sec. IVC), the leave-one-out analysis (Sec. VB1), and the out-of-sample cross-predictions in Analyses A and B are carefully executed, and the authors are commendably transparent about the flat-prior disagreement among parametrizations (Fig. 11) and about the tension with the photon-tagged jet RAA (Sec. VD). I confirm the stress-test concern: the Sec. VC prior is load-bearing for the super-Casimir claim, and its window conflicts with the paper's own estimate of the number of resolved emitters, with the posterior sitting at the prior boundary. If the claim survived a reconciled prior choice, it would point to genuinely interesting multi-parton quenching effects beyond Casimir scaling; as it stands, the claim is conditional on the prior rather than established by the data, while the universality result is better supported but requires qualification given the photon-tagged RAA over-prediction.

major comments (3)
  1. [Sec. VC; Eq. (6); Table III; Secs. IIA/IIB] The prior restriction that drives the central CR ≈ 3.5 result is internally inconsistent with the paper's own estimate of the number of resolved emitters. Section IIA quotes realistic calculations giving n_i ≲ 2–3, and Sec. IIB identifies the gamma shape parameter as α = 1 + n; together these imply α ≈ 3–4 and ⟨ε⟩/ε_max = α/(α−1) ≈ 1.33–1.5, which through Eq. (6) corresponds to α_s ≈ 0.67–0.75. Table III instead restricts the gamma distribution to α ∈ [1.11, 2], i.e. n ∈ [0.11, 1] and 1/α_s ≈ 2–10, so the region preferred by the paper's own physics discussion is excluded before the data are considered. Moreover, the posterior for the gamma parametrization prefers α_s near the lower boundary of the imposed window (Sec. VC), so the convergence of all three parametrizations to CR ≈ 3.5 in Fig. 14 may reflect the shared prior boundary rather than information from the data. The authors should either reconcile the α = 1 + n interpretation with the imposed α window, or demonstrate that CR is stable when the window is extended into the n ≲ 3 region; a closure test with truth CR = 2.25 under the restricted prior would be a useful check that the machinery does not bias the recovered ratio toward the boundary.
  2. [Abstract; Sec. VB (Fig. 11); Sec. VI] With flat priors the three parametrizations give mutually inconsistent color ratios: CR ≈ 3.5 (normal), ≈ 1 (log-normal), and ≈ 2.25 (gamma) (Fig. 11, right column), a disagreement the paper explicitly acknowledges in Sec. VC. The abstract nevertheless states that "we extract that the color dependence of energy loss is slightly bigger than what expected from Casimir scaling," and Sec. VI concludes that "we have established evidence of color charge dependence of energy loss," without the caveat that these statements hold only under the restricted prior of Sec. VC. Given the paper's stated philosophy of data-driven extraction with "minimal assumptions from theory" (Sec. I), the central claim should be presented explicitly as conditional on the Sec. VC prior, or the flat-prior disagreement should be resolved by an argument that rules out the log-normal and gamma parametrizations.
  3. [Sec. VD; Fig. 16] The universality claim in Sec. VA is qualified by the behavior of the photon-tagged jet RAA, an observable that is excluded from the main analysis. When predicted from the global analysis with nPDFs, this observable is over-predicted by about 50% at pT ≈ 100 GeV (textured bands, upper panel of Fig. 16), and the paper concludes that "there is a certain tension between the inclusive jet and photon-tagged jet data" (Sec. VD). The abstract's assertion that the analysis establishes "the consistency between different data-sets" therefore goes beyond what Sec. VD reports; the qualification given in the body text should also appear in the abstract and conclusions.
minor comments (6)
  1. [Secs. I–III] The manuscript contains numerous typos and duplicated words, including "to loose energy" (Sec. IIA), "gluon-initiated jets loosing more energy" (Sec. VB), "can take place occur" (Sec. II), "For for Pb-Pb central collisions" (Sec. III), "the the local conditions" (Sec. I), and "mean energy energy loss" (Sec. IIA); these should be corrected.
  2. [Fig. 2 caption] The caption begins with "Left panel:" and then "Left panels:" for the two photon-tagged panels, which is confusing; the three panels should be referenced unambiguously.
  3. [Table II] The reduced chi-square values for the global fit are 2.28 (normal), 1.41 (log-normal), and 1.70 (gamma), i.e. notably above unity for two of the three parametrizations; the paper does not comment on whether these values indicate a formally poor fit and what that implies for the reliability of the corresponding extraction.
  4. [Sec. IVC, Fig. 4] The closure tests validate the inference machinery within each parametrization family, but the Fig. 4 caption notes that the mock data are chosen "with a specific offset with respect to the experimental data"; reporting the actual truth values used, in particular the truth CR, would help the reader judge whether the closure tests cover the CR range relevant to the super-Casimir claim.
  5. [Sec. IIIA; App. B] Only the EPS09 nPDF set is used in the baseline; given the demonstrated sensitivity of the high-pT inclusive RAA and the photon-tagged jet RAA to nPDF effects (Fig. 3 and App. B), the authors should comment on the potential impact of more recent nPDF sets (e.g., EPPS21 or nNNPDF3.0) on the extracted CR.
  6. [Ref. [8]] The citation "JHEP21, 206 (2020)" appears to have a malformed volume field; the format should be checked against the journal's style.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation is an explicit fit to external LHC data with genuine out-of-sample predictions; the Sec. VC prior is transparent and does not encode the target color ratio.

full rationale

The paper's central claims are (i) universality of the quark/gluon quenching weight and (ii) a super-Casimir color ratio CR≈3.5. Both are obtained from explicit Bayesian fits to ATLAS data, and the universality claim rests on genuine out-of-sample predictions: Analysis A fits inclusive jet observables and predicts photon-tagged jet yields, while Analysis B does the reverse (Sec. VA, Figs. 7 and 8). These predictions are not forced by construction because the model output for each observable is a different convolution of the same D(ε) with a different baseline spectrum. The color-ratio extraction in Sec. VC uses a prior restriction based on Eq. (6), <ε>/ε_max ~ 1/α_s with 0.1 < α_s < 0.5, implemented symmetrically for quark and gluon parameters (Table III). This prior does not incorporate the target CR > N_c/C_F = 2.25; it restricts the mean-to-mode ratio of each distribution independently, so the convergence of all three parametrizations to CR≈3.5 is a data-driven outcome rather than an algebraic consequence of the prior. The paper itself flags the fragility of the result: with flat priors the three parametrizations give CR≈3.5, ≈1, and ≈2.25 (Fig. 11), and the Sec. VC prior is admittedly needed to break the ambiguity. It also acknowledges that the predicted photon-tagged jet RAA over-predicts suppression by about 50% at pT≈100 GeV (Sec. VD). These are prior-dependence and model-limitation concerns, not circularity: the prior is stated, the target is not encoded in it, and the extracted value is not used to define the model. Likewise, the cited theoretical calculations, including the n_i ≲ 2–3 estimate from the authors' earlier work, are context for interpreting the result rather than inputs that force the posterior. No equation in the paper is equivalent by construction to the paper's conclusions, and no fitted parameter is renamed as a prediction. The analysis is therefore self-contained relative to the experimental benchmarks, and the limitations are openly disclosed.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claims rest on fitted energy-loss parameters (four per parametrization), baseline spectral fits to PYTHIA8, and the ad hoc prior restriction used to stabilize the color-ratio extraction. No new entities are introduced.

free parameters (5)
  • Normal parameters (mu_q, mu_g, sigma_q, sigma_g) = Posterior distributions
    Parameters of Eq. (10), inferred from data.
  • Log-normal parameters (mu_q, mu_g, sigma_q, sigma_g) = Posterior distributions
    Parameters of Eq. (12).
  • Gamma parameters (<epsilon_q>, <epsilon_g>, alpha_q, alpha_g) = Posterior distributions
    Parameters of Eq. (15).
  • Baseline spectral fit coefficients A0, n_i(pT) = Not listed
    Fitted to PYTHIA8 spectra in Eqs. (23) and (29).
  • Prior restriction ranges (xi, sigma, alpha) = xi in [0.08,0.51], sigma in [0.68,1.24], alpha in [1.11,2]
    Chosen in Sec. VC (Table III) to impose alpha_s in (0.1,0.5); load-bearing for the color-ratio result.
assumptions (6)
  • domain assumption Factorization of the jet spectrum: sigma_med(pT) = D (x) sigma_vac(pT+epsilon) (Eq. 1)
    Assumes separation of hard production and medium modification; stated in Sec. I and II.
  • domain assumption Event-averaged universal D(epsilon) for fixed centrality (0-10%) and R=0.4, independent of jet pT
    Working hypothesis 1 in Sec. II; neglects pT dependence and medium fluctuations.
  • domain assumption Energy loss is a sum of independent Poissonian emissions (Eq. 3)
    Working hypothesis 2 in Sec. II; used to derive Eq. (6) and interpret color ratio.
  • domain assumption EPS09 nuclear PDFs describe nuclear modifications of the baseline
    Used in Sec. IIIA to build the AA baseline; nPDF effects reach about 20% at high pT (Fig. 3).
  • domain assumption PYTHIA8 leading-order baseline reproduces measured pp spectra
    Used in Sec. IIIA/IIIB; agreement with ATLAS data shown in Fig. 2.
  • domain assumption Mean-to-mode ratio ~ 1/alpha_s with 0.1 < alpha_s < 0.5 (Eq. 6) as a prior constraint
    Derived from a simple multiple-scattering model in Sec. IIA and applied as a prior in Sec. VC (Table III); it is load-bearing for the color-ratio result.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Constraining Jet Quenching in Heavy-Ion Collisions with Bayesian Inference." pith.science (2026). https://pith.science/paper/NMUICDS5

@misc{pith2026241114552,
  author       = {Pith},
  title        = {Pith review of: Constraining Jet Quenching in Heavy-Ion Collisions with Bayesian Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NMUICDS5}},
  note         = {Machine review of arXiv:2411.14552}
}
abstract

Jet suppression and modification is a hallmark feature of heavy-ion collisions. This can be attributed to an accumulated set of effects, including radiative and elastic energy loss and reabsorption of thermalized energy within the jet cone, which are encoded in a quenching weight, determining the probability distribution for a shift of the $p_T$ (energy loss). We perform a data-driven analysis, based on Bayesian inference, to extract information about the energy-loss distribution experienced by propagating jets using generic and flexible parametrizations. We first establish the consistency between different data-sets and, thereby, provide evidence for the universality of the quark/gluon quenching weights for different observables. Furthermore, we extract that the color dependence of energy loss is slightly bigger than what expected from Casimir scaling, pointing to the importance of multi-parton quenching within high-$p_T$ jets at the LHC.

Figures

Figures reproduced from arXiv: 2411.14552 by the authors.

Figure 1
Figure 1. FIG. 1. Upper panel: fraction of inclusive jets initiated by [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Left panel: inclusive jet cross-section as a function of jet [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. nPDF effect on the inclusive jet cross-section (left panel), and on the photon-tagged jet yield (upper left panel) and [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: FIG. 4. An example on how the closure tests are performed [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Posterior distributions obtained for the closure tests presented in the form of a corner plot. The posteriors for the [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Prior and posterior distributions of the mean energy [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. Posterior distributions and respective correlations, in the form of a corner plot, for the three energy-loss parametriza [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. 90% HDI of the posterior predicative distributions obtained for analysis A (upper panels) and analysis B (lower panels) [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. Posterior distributions and respective correlations, in the form of a corner plot, for the three energy-loss parametriza [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: FIG. 10. 90% HDI of the posterior predicative distributions obtained for the global analysis, for all the observables in the [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: FIG. 11. Prior and posterior distributions of the mean energy [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: FIG. 12. Same as above, when prior constraining is used. [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: FIG. 13. Same as above, when prior constraining is used. [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 14
Figure 14. Figure 14: FIG. 14. Same as above, when prior constraining is used. [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: FIG. 15. Posterior mean and 90% HDI of the energy loss [PITH_FULL_IMAGE:figures/full_fig_p019_15.png]
Figure 16
Figure 16. Figure 16: FIG. 16. 90% HDI of the posterior predictive distribution [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]
Figure 17
Figure 17. Figure 17: FIG. 17. Fits to the PYTHIA8 [PITH_FULL_IMAGE:figures/full_fig_p022_17.png]
Figure 18
Figure 18. Figure 18: FIG. 18. Fits to the PYTHIA8 nucleus-nucleus collisions sampled inclusive jet cross-section (left panel), and on the photon [PITH_FULL_IMAGE:figures/full_fig_p022_18.png]
Figure 19
Figure 19. Figure 19: FIG. 19. 90% HDI of the posterior predicative distributions obtained for all the observables in the study, when Bayesian [PITH_FULL_IMAGE:figures/full_fig_p023_19.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Visualizing How the Structure of Large-Radius Jets Shapes Their Wakes

    hep-ph 2025-01 conditional novelty 7.0 of 10

    Hybrid Model simulations show ATLAS large-radius jet data rule out fully coherent jet energy loss, and low-pT jet-shape observables can visualize merging subjet wakes.

  2. A simple model to investigate jet quenching and correlated errors for centrality-dependent nuclear-modification factors in relativistic heavy-ion collisions

    nucl-th 2024-12 accept novelty 6.0 of 10

    A simple two-parameter jet energy-loss model can describe centrality-dependent jet suppression measurements, but the inferred formation time depends on the assumed path-length scaling and on how systematic errors are ...

  3. Hydrodynamic Description of the Quark-Gluon Plasma

    nucl-th 2024-12 unverdicted

    A review of how relativistic hydrodynamics has been used to discover and quantify the quark-gluon plasma in heavy-ion collisions.

Reference graph

Works this paper leans on

99 extracted references · 7 canonical work pages · cited by 3 Pith papers

  1. [1]

    The former is dominated by the leading-order contribution to the hard cross-sections, while the latter cases arise at higher-order processes

    Per-photon jet yield The photon-tagged jet yield exhibits a more compos- ite distribution, interpolating between the case when the pT’s of the jet and photon are balanced and when they are not, typically we considerpjet T > pγ T. The former is dominated by the leading-order contribution to the hard cross-sections, while the latter cases arise at higher-or...

  2. [2]

    It is defined and described precisely in the same way as the inclusiveRAA in Sec

    Photon-tagged jet spectrum and RAA We also have considered recent measurements of the nuclear modification factorRAA for photon-tagged jets [69]. It is defined and described precisely in the same way as the inclusiveRAA in Sec. IIIA. The pp spectra are also generated in the same way as for inclusive jets, see Sec. IIIA, with jets for which a photon is fou...

  3. [3]

    3 we plot the nPDF effects on the spectrum

    Establishing the baseline for jet energy loss In Fig. 3 we plot the nPDF effects on the spectrum. In the ratio ofAA topp data, a significant effect is again recovered and it reaches a deviation of 20% at highpT for inclusive jets(left panel), and a general deviation also of about 20% forγ-jets (lower right panel). It is impor- 10 tant to note this base co...

  4. [4]

    Leave-one-out study on the constraining power of each observable The leave-one-out method, widely used in machine learning approaches to validate a specific model, can be usedheretoassesstheconstrainingpowerofeachindivid- ual observable. In this example, inference is performed on all observables but one, and the posterior predictive distribution is comput...

  5. [5]

    Mean jet energy-loss and color dependence The posterior distribution of the mean jet energy-loss P (⟨εi⟩|x) quark- and gluon-initiated jets and the pos- terior distribution of the color ratio P (CR|x) can be computed for all three parametrizations as described in Sec. IVC. Figure 11 shows the posterior distributions for the mean jet energy-loss (left colu...

  6. [6]

    Busza, K

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

    arXiv 2018

  7. [7]

    Apolinário, Y.-J

    L. Apolinário, Y.-J. Lee, and M. Winn, Prog. Part. Nucl. Phys. 127, 103990 (2022), arXiv:2203.16352 [hep-ph]

    arXiv 2022

  8. [8]

    Cunqueiro and A

    L. Cunqueiro and A. M. Sickles, Prog. Part. Nucl. Phys. 124, 103940 (2022), arXiv:2110.14490 [nucl-ex]

    arXiv 2022

Show all 99 references
  1. [9]

    Cunqueiro, D

    L. Cunqueiro, D. Pablos, A. Soto-Ontoso, M. Spousta, A. Takacs, and M. Verweij, Phys. Rev. D110, 014015 (2024), arXiv:2311.07643 [hep-ph]

    2024 arXiv

  2. [10]

    Baier, Y

    R. Baier, Y. L. Dokshitzer, A. H. Mueller, and D. Schiff, JHEP 09, 033 (2001), arXiv:hep-ph/0106347. 24

    2001 arXiv

  3. [11]

    Rajagopal, A

    K. Rajagopal, A. V. Sadofyev, and W. van der Schee, Phys. Rev. Lett.116, 211603 (2016), arXiv:1602.04187 [nucl-th]

    2016 arXiv

  4. [12]

    Casalderrey-Solana, Z

    J. Casalderrey-Solana, Z. Hulcher, G. Milhano, D. Pab- los, and K. Rajagopal, Phys. Rev. C99, 051901 (2019), arXiv:1808.07386 [hep-ph]

    2019 arXiv

  5. [13]

    Y.-L. Du, D. Pablos, and K. Tywoniuk, JHEP21, 206 (2020), arXiv:2012.07797 [hep-ph]

    2020 arXiv

  6. [14]

    Y.-L. Du, D. Pablos, and K. Tywoniuk, Phys. Rev. Lett. 128, 012301 (2022), arXiv:2106.11271 [hep-ph]

    2022 arXiv

  7. [15]

    Brewer, J

    J. Brewer, J. G. Milhano, and J. Thaler, Phys. Rev. Lett. 122, 222301 (2019), arXiv:1812.05111 [hep-ph]

    2019 arXiv

  8. [16]

    Takacs and K

    A. Takacs and K. Tywoniuk, JHEP 10, 038 (2021), arXiv:2103.14676 [hep-ph]

    2021 arXiv

  9. [17]

    Apolinário, L

    L. Apolinário, L. Luís, J. G. Milhano, and J. a. M. Silva, (2024), arXiv:2409.12238 [hep-ph]

    2024 arXiv

  10. [18]

    At leading order, the momentum transfer is purely trans- verse to the direction of the jet particles and scales as ˆq∝ϵ3/4, whereϵ is the energy density of the medium

  11. [19]

    K. M. Burke et al. (JET), Phys. Rev. C 90, 014909 (2014), arXiv:1312.5003 [nucl-th]

    2014 arXiv

  12. [20]

    M. Xie, W. Ke, H. Zhang, and X.-N. Wang, Phys. Rev. C 108, L011901 (2023), arXiv:2206.01340 [hep-ph]

    2023 arXiv

  13. [21]

    M. Xie, W. Ke, H. Zhang, and X.-N. Wang, (2022), arXiv:2208.14419 [hep-ph]

    2022 arXiv

  14. [22]

    Ehlerset al

    R. Ehlerset al. (JETSCAPE), (2024), arXiv:2408.08247 [hep-ph]

    2024

  15. [23]

    Y. Xu, J. E. Bernhard, S. A. Bass, M. Nahrgang, and S. Cao, Phys. Rev. C 97, 014907 (2018), arXiv:1710.00807 [nucl-th]

    2018 arXiv

  16. [24]

    He, L.-G

    Y. He, L.-G. Pang, and X.-N. Wang, Phys. Rev. Lett. 122, 252302 (2019), arXiv:1808.05310 [hep-ph]

    2019 arXiv

  17. [25]

    W. Ke, Y. Xu, and S. A. Bass, Phys. Rev. C98, 064901 (2018), arXiv:1806.08848 [nucl-th]

    2018 arXiv

  18. [26]

    Ke and X.-N

    W. Ke and X.-N. Wang, JHEP 05, 041 (2021), arXiv:2010.13680 [hep-ph]

    2021 arXiv

  19. [27]

    Everett et al

    D. Everett et al. (JETSCAPE), Phys. Rev. C 103, 054904 (2021), arXiv:2011.01430 [hep-ph]

    2021 arXiv

  20. [28]

    Cao et al

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

    2021 arXiv

  21. [29]

    J. Wu, W. Ke, and X.-N. Wang, Phys. Rev. C 108, 034911 (2023), arXiv:2304.06339 [hep-ph]

    2023 arXiv

  22. [30]

    Zhang, E

    S.-L. Zhang, E. Wang, H. Xing, and B.-W. Zhang, Phys. Lett. B850, 138549 (2024), arXiv:2303.14881 [hep-ph]

    2024 arXiv

  23. [31]

    Fanet al

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

    2024 arXiv

  24. [32]

    Novak, K

    J. Novak, K. Novak, S. Pratt, J. Vredevoogd, C. Coleman-Smith, and R. Wolpert, Phys. Rev. C89, 034917 (2014), arXiv:1303.5769 [nucl-th]

    2014 arXiv

  25. [33]

    J. E. Bernhard, P. W. Marcy, C. E. Coleman-Smith, S. Huzurbazar, R. L. Wolpert, and S. A. Bass, Phys. Rev. C91, 054910 (2015), arXiv:1502.00339 [nucl-th]

    2015 arXiv

  26. [34]

    J. E. Bernhard, J. S. Moreland, S. A. Bass, J. Liu, and U. Heinz, Phys. Rev. C 94, 024907 (2016), arXiv:1605.03954 [nucl-th]

    2016 arXiv

  27. [35]

    J. E. Bernhard,Bayesian parameter estimation for rela- tivistic heavy-ion collisions,Ph.D.thesis,DukeU.(2018), arXiv:1804.06469 [nucl-th]

    2018 arXiv

  28. [36]

    J. E. Bernhard, J. S. Moreland, and S. A. Bass, Nature Phys. 15, 1113 (2019)

    2019

  29. [37]

    Everett et al

    D. Everett et al. (JETSCAPE), Phys. Rev. Lett.126, 242301 (2021), arXiv:2010.03928 [hep-ph]

    2021 arXiv

  30. [38]

    Auvinen, J

    J. Auvinen, J. E. Bernhard, S. A. Bass, and I. Karpenko, Phys. Rev. C97, 044905 (2018), arXiv:1706.03666 [hep- ph]

    2018 arXiv

  31. [39]

    G. Nijs, W. van der Schee, U. Gürsoy, and R. Snellings, Phys. Rev. Lett.126, 202301 (2021), arXiv:2010.15130 [nucl-th]

    2021 arXiv

  32. [40]

    Pratt, E

    S. Pratt, E. Sangaline, P. Sorensen, and H. Wang, Phys. Rev. Lett. 114, 202301 (2015), arXiv:1501.04042 [nucl- th]

    2015 arXiv

  33. [41]

    N.Armesto, J.Rojo, C.A.Salgado, andP.Zurita,JHEP 11, 015 (2013), arXiv:1309.5371 [hep-ph]

    2013 arXiv

  34. [42]

    Paquet, (2023), arXiv:2310.17618 [nucl-th]

    J.-F. Paquet, (2023), arXiv:2310.17618 [nucl-th]

    2023 arXiv

  35. [43]

    S. Cao, A. Majumder, R. Modarresi-Yazdi, I. Soudi, and Y. Tachibana, (2024), arXiv:2401.10026 [hep-ph]

    2024 arXiv

  36. [44]

    Spousta and B

    M. Spousta and B. Cole, Eur. Phys. J. C76, 50 (2016), arXiv:1504.05169 [hep-ph]

    2016 arXiv

  37. [45]

    Spousta, Phys

    M. Spousta, Phys. Lett. B 767, 10 (2017), arXiv:1606.00903 [hep-ph]

    2017 arXiv

  38. [46]

    J.-W. Qiu, F. Ringer, N. Sato, and P. Zurita, Phys. Rev. Lett. 122, 252301 (2019), arXiv:1903.01993 [hep-ph]

    2019 arXiv

  39. [47]

    [12, 94] found a similar quenching pattern for quark- and gluon-initiated jets within the JEWEL Monte-Carlo model

    We note that Refs. [12, 94] found a similar quenching pattern for quark- and gluon-initiated jets within the JEWEL Monte-Carlo model

  40. [48]

    Brewer, J

    J. Brewer, J. Thaler, and A. P. Turner, Phys. Rev. C 103, L021901 (2021), arXiv:2008.08596 [hep-ph]

    2021 arXiv

  41. [49]

    C. A. Salgado and U. A. Wiedemann, Phys. Rev. D68, 014008 (2003), arXiv:hep-ph/0302184

    2003 arXiv

  42. [50]

    Arleo, JHEP11, 044 (2002), arXiv:hep-ph/0210104

    F. Arleo, JHEP11, 044 (2002), arXiv:hep-ph/0210104

    2002 arXiv

  43. [51]

    Zhang, J

    S.-L. Zhang, J. Liao, G.-Y. Qin, E. Wang, and H. Xing, Sci. Bull.68, 2003 (2023), arXiv:2208.08323 [hep-ph]

    2023 arXiv

  44. [52]

    W.-J. Xing, S. Cao, and G.-Y. Qin, Phys. Lett. B850, 138523 (2024), arXiv:2303.12485 [hep-ph]

    2024 arXiv

  45. [53]

    Casalderrey-Solana, E

    J. Casalderrey-Solana, E. V. Shuryak, and D. Teaney, (2006), arXiv:hep-ph/0602183

    2006 arXiv

  46. [54]

    G. Y. Qin, A. Majumder, H. Song, and U. Heinz, Phys. Rev. Lett.103, 152303 (2009), arXiv:0903.2255 [nucl-th]

    2009 arXiv

  47. [55]

    Tachibana, N.-B

    Y. Tachibana, N.-B. Chang, and G.-Y. Qin, Phys. Rev. C 95, 044909 (2017), arXiv:1701.07951 [nucl-th]

    2017 arXiv

  48. [56]

    W. Chen, S. Cao, T. Luo, L.-G. Pang, and X.-N. Wang, Phys. Lett. B777, 86 (2018), arXiv:1704.03648 [nucl-th]

    2018 arXiv

  49. [57]

    G.-Y. Qin, J. Ruppert, C. Gale, S. Jeon, G. D. Moore, and M. G. Mustafa, Phys. Rev. Lett.100, 072301 (2008), arXiv:0710.0605 [hep-ph]

    2008 arXiv

  50. [58]

    S. Cao, T. Luo, G.-Y. Qin, and X.-N. Wang, Phys. Rev. C 94, 014909 (2016), arXiv:1605.06447 [nucl-th]

    2016 arXiv

  51. [59]

    Mehtar-Tani and K

    Y. Mehtar-Tani and K. Tywoniuk, Phys. Rev. D 98, 051501 (2018), arXiv:1707.07361 [hep-ph]

    2018 arXiv

  52. [60]

    Caucal, E

    P. Caucal, E. Iancu, A. H. Mueller, and G. Soyez, Phys. Rev. Lett. 120, 232001 (2018), arXiv:1801.09703 [hep- ph]

    2018 arXiv

  53. [61]

    Dasgupta, F

    M. Dasgupta, F. Dreyer, G. P. Salam, and G. Soyez, JHEP 04, 039 (2015), arXiv:1411.5182 [hep-ph]

    2015 arXiv

  54. [62]

    Dasgupta, F

    M. Dasgupta, F. A. Dreyer, G. P. Salam, and G. Soyez, JHEP 06, 057 (2016), arXiv:1602.01110 [hep-ph]

    2016 arXiv

  55. [63]

    Z.-B. Kang, F. Ringer, and I. Vitev, JHEP 10, 125 (2016), arXiv:1606.06732 [hep-ph]

    2016 arXiv

  56. [64]

    HereDrad(ε1) andDrad(ε1) are the energy loss distributions due to radiative and elastic processes, re- spectively

    These results generalize straightforwardly when includ- ing elastic energy losses asD(ε) = ∫ dε1 ∫ dε2δ(ε−ε1− ε2)Drad(ε1)Del(ε2), such that⟨ε⟩ =⟨εrad⟩1 +⟨εel⟩1, and so on. HereDrad(ε1) andDrad(ε1) are the energy loss distributions due to radiative and elastic processes, re- spectively

  57. [65]

    Baier, Y

    R. Baier, Y. L. Dokshitzer, A. H. Mueller, S. Peigne, and D. Schiff, Nucl. Phys. B483, 291 (1997), arXiv:hep- ph/9607355

    1997

  58. [66]

    Abreu, X

    S. Abreu, X. Mayo López, G. Milhano, and A. Soto- 25 Ontoso, (2024), arXiv:2410.24135 [hep-ph]

    2024 arXiv

  59. [67]

    Mehtar-Tani, D

    Y. Mehtar-Tani, D. Pablos, and K. Tywoniuk, Phys. Rev. Lett. 127, 252301 (2021), arXiv:2101.01742 [hep- ph]

    2021 arXiv

  60. [68]

    Mehtar-Tani, D

    Y. Mehtar-Tani, D. Pablos, and K. Tywoniuk, (2024), arXiv:2402.07869 [hep-ph]

    2024 arXiv

  61. [69]

    Kumar and P

    V. Kumar and P. Shukla, Eur. Phys. J. A60, 169 (2024), arXiv:2410.07852 [hep-ph]

    2024 arXiv

  62. [70]

    Ogrodnik, M

    A. Ogrodnik, M. Rybár, and M. Spousta, (2024), arXiv:2407.11234 [hep-ph]

    2024

  63. [71]

    Pablos and A

    D. Pablos and A. Soto-Ontoso, Phys. Rev. D107, 094003 (2023), arXiv:2210.07901 [hep-ph]

    2023 arXiv

  64. [72]

    Aaboud et al

    M. Aaboud et al. (ATLAS), Phys. Lett. B 790, 108 (2019), arXiv:1805.05635 [nucl-ex]

    2019 arXiv

  65. [73]

    Aaboud et al

    M. Aaboud et al. (ATLAS), Phys. Lett. B 789, 167 (2019), arXiv:1809.07280 [nucl-ex]

    2019 arXiv

  66. [74]

    Aadet al

    G. Aadet al. (ATLAS), (2023), arXiv:2303.10090 [nucl- ex]

    2023 arXiv

  67. [75]

    Sjostrand, S

    T. Sjostrand, S. Mrenna, and P. Z. Skands, JHEP05, 026 (2006), arXiv:hep-ph/0603175

    2006 arXiv

  68. [76]

    Sjostrand, S

    T. Sjostrand, S. Mrenna, and P. Z. Skands, Comput. Phys. Commun.178, 852 (2008), arXiv:0710.3820 [hep- ph]

    2008 arXiv

  69. [77]

    Cacciari, G

    M. Cacciari, G. P. Salam, and G. Soyez, Eur. Phys. J. C 72, 1896 (2012), arXiv:1111.6097 [hep-ph]

    2012 arXiv

  70. [78]

    K. J. Eskola, H. Paukkunen, and C. A. Salgado, JHEP 04, 065 (2009), arXiv:0902.4154 [hep-ph]

    2009 arXiv

  71. [79]

    R. K. Ellis, W. J. Stirling, and B. R. Webber, QCD and collider physics , Vol. 8 (Cambridge University Press, 2011)

    2011

  72. [80]

    Here we have used the standard approximation, i.e.∫∞ 0 εD (ε)/(pT +ε)n≃p−n T ∫∞ 0 dεD (ε)e−nε/pT

  73. [81]

    Andrieu, N

    C. Andrieu, N. de Freitas, A. Doucet, and M. I. Jordan, Machine Learning50, 5–43 (2003)

    2003

  74. [82]

    Albert and J

    J. Albert and J. Hu,Probability and Bayesian Modeling (Chapman and Hall/CRC, 2019)

    2019

  75. [83]

    von Toussaint, Rev

    U. von Toussaint, Rev. Mod. Phys.83, 943 (2011)

    2011

  76. [84]

    D’Agostini, Rept

    G. D’Agostini, Rept. Prog. Phys. 66, 1383 (2003), arXiv:physics/0304102

    2003 arXiv

  77. [85]

    C. K. Williams and C. E. Rasmussen,Gaussian processes for machine learning, Vol. 2 (MIT press Cambridge, MA, 2006)

    2006

  78. [86]

    C. M. Bishop and N. M. Nasrabadi,Pattern recognition and machine learning , Vol. 4 (Springer, 2006)

    2006

  79. [87]

    C. E. Rasmussen and C. K. I. Williams,Gaussian Pro- cesses for Machine Learning (The MIT Press, 2005)

    2005

  80. [88]

    Weiss, J.-F

    B. Weiss, J.-F. Paquet, and S. A. Bass, J. Phys. G50, 065104 (2023), arXiv:2301.08385 [nucl-th]

    2023 arXiv

  81. [89]

    G. Nijs, W. van der Schee, U. Gürsoy, and R. Snellings, Phys. Rev. C 103, 054909 (2021), arXiv:2010.15134 [nucl-th]

    2021 arXiv

  82. [90]

    Chatrchyan et al

    S. Chatrchyan et al. (CMS), Phys. Rev. C84, 024906 (2011), arXiv:1102.1957 [nucl-ex]

    2011 arXiv

  83. [91]

    Khachatryan et al

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

    2017 arXiv

  84. [92]

    A. M. Sirunyan et al. (CMS), Phys. Lett. B 785, 14 (2018), arXiv:1711.09738 [nucl-ex]

    2018 arXiv

  85. [93]

    Adam et al

    J. Adam et al. (ALICE), Phys. Lett. B746, 1 (2015), arXiv:1502.01689 [nucl-ex]

    2015 arXiv

  86. [94]

    Adam et al

    J. Adam et al. (ALICE), JHEP 09, 170 (2015), arXiv:1506.03984 [nucl-ex]

    2015 arXiv

  87. [95]

    A. M. Sirunyan et al. (CMS), JHEP 05, 284 (2021), arXiv:2102.13080 [hep-ex]

    2021 arXiv

  88. [96]

    Aad et al

    G. Aad et al. (ATLAS), Phys. Rev. Lett.131, 172301 (2023), arXiv:2301.05606 [nucl-ex]

    2023 arXiv

  89. [97]

    Since the quark non-singlet decouples from the gluons, this feature is protected by DGLAP evolution

    These effects are remnants of the EMC effect and is pa- rameterized from nuclear DIS data at low-Q. Since the quark non-singlet decouples from the gluons, this feature is protected by DGLAP evolution

  90. [98]

    standard

    We have also not observed any particular effect of remov- ing the nPDFs from our “standard” global analysis

  91. [99]

    Apolinário, J

    L. Apolinário, J. a. Barata, and G. Milhano, Eur. Phys. J. C80, 586 (2020), arXiv:2003.02893 [hep-ph]

    2020 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.