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

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

Dongguk Kim , Dongjo Kim , Jeongsu Bok , Beomkyu Kim This is my paper

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

classification ✦ hep-ph
keywords acrosscollisionsconstraintsenergypredictivebayesianbeamcentrality
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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.

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