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REVIEW 2 major objections 4 minor 114 references

Search for resonant production of lepton-enriched semivisible jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV

T0 review · 2 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read First search for lepton-enriched semivisible jets finds no signal and excludes Z' mediators up to 4.7 TeV at 95% confidence.

desk verdict Solid first search for lepton-enriched semivisible jets; exclusions are plausible, but the ML ABCD closure validation has a circularity worth probing. read the letter →

arxiv 2608.05323 v1 pith:JGX2WG5J submitted 2026-08-05 hep-ex

CMS Collaboration This is my paper · ORCID
classification hep-ex
keywords semivisiblejetsdarksectorhiddenvalleyZprimemediatormissingtransversemomentumgraphneuralnetworkABCDmethodLHCsearches
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports the first search for lepton-enriched semivisible jets, a signature of a strongly coupled dark sector in which jets of dark hadrons contain both stable invisible particles and prompt decays to standard-model quarks and leptons, so missing transverse momentum aligns with the jet. Using 138 fb$^{-1}$ of 13 TeV proton-proton collision data, the analysis finds the data consistent with standard-model background and sets 95% confidence-level exclusion limits on the $Z'$ mediator mass: up to 4.7 TeV for the all-lepton ($\mathrm{SVJ}_\ell$) model and between 1.8 and 3.5 TeV for the tau-enriched ($\mathrm{SVJ}_\tau$) model. The largest observed deviation, at $m_{Z'}=3$ TeV with $r_{\mathrm{inv}}=0.7$, has a local significance of 2.4 standard deviations and a global significance of 1.6, consistent with a background fluctuation. These are the first experimental constraints on lepton-enriched semivisible jets and open a previously unprobed part of dark-shower parameter space.

What carries the argument

The central object is the semivisible jet (SVJ): a collimated spray produced when a $Z'$ resonance decays to dark quarks that shower under a confining dark QCD force, forming both stable invisible dark hadrons and unstable dark hadrons that decay promptly to standard-model quarks and leptons. This mixture makes the jet's missing transverse momentum point roughly along the jet axis, and the dijet transverse mass $m_T$ peaks at $m_{Z'}$. The analysis chain is carried by two machine-learning systems: a graph-neural-network tagger (LUNDNET), fed with $k_T$-pruned Lund-tree representations of the two leading jets and extended with particle-type energy fractions so it can spot lepton content; and the MD-ABCDISCOTEC background estimator, which trains two discriminators $D_1$ and $D_2$ to be statistically independent of each other and of $m_T$, so the ABCD relation $N_A=N_B N_C/N_D$ can predict the background in each $m_T$ bin of the signal region.

What would settle it

Measure the distance correlation between D1 and D2 on a background-dominated sample inside the low-$\Delta$-eta signal region (for example, by inverting the lepton-isolation requirement or shifting the ABCD boundaries), and compare the ABCD prediction N_A = N_B N_C / N_D with the observed yield bin-by-bin in mT; a residual correlation larger than the quoted 0.1-15% normalization uncertainty in the high-mT bins would shift the exclusion ranges.

Watch

Extended reading notes

Core claim

The paper's central claim is that, for the benchmark strongly coupled dark-sector models with a $Z'$ mediator coupled to standard-model quarks with strength $g_q=0.25$, no resonant lepton-enriched semivisible-jet signal is present in the data. The observed $m_T$ spectra in the signal regions agree with the background predicted by the mass-decorrelated ABCD method, and the resulting 95% CL upper limits on $\sigma(Z') B_{\mathrm{dark}}$ exclude mediator masses up to 4.7 TeV for the $\mathrm{SVJ}_\ell$ scenario and from 1.8 to 3.5 TeV for the $\mathrm{SVJ}_\tau$ scenario, depending on the model parameters. This is the first time the lepton-enriched versions of the semivisible-jet signature have been constrained experimentally, and the analysis achieves it by combining a LUNDNET graph-neural-network tagger sensitive to dark-shower substructure with a background-estimation network that enforces decorrelation between its two discriminators and from $m_T$ itself.

Load-bearing premise

The background estimate assumes that the two neural-network discriminators D1 and D2 are statistically independent for all standard-model backgrounds across every mT bin used in the fit, and this independence is enforced and validated in training and in a high-$\Delta$-eta control region rather than directly inside the low-$\Delta$-eta signal region.

Editorial extensions

If this is right

  • For the benchmark couplings assumed, a wide class of strongly coupled dark-sector models with prompt leptonic dark-hadron decays is now excluded at 95% confidence level for $Z'$ masses up to 4.7 TeV in the all-lepton scenario and between 1.8 and 3.5 TeV in the tau-enriched scenario.
  • The lepton-enriched searches cover parameter space that fully hadronic semivisible-jet searches cannot reach, so the two types of results together bound a broader set of dark-shower models.
  • The local 2.4-standard-deviation excess at $m_{Z'}=3$ TeV and $r_{\mathrm{inv}}=0.7$ is not significant once the look-elsewhere effect is accounted for, so it does not support a discovery claim.
  • The MD-ABCDISCOTEC extension and the lepton-aware LUNDNET tagger are demonstrated on a resonant search and can be transferred to other analyses that need an ABCD estimate in bins of a mass-like variable.

Reading between the lines

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

  • Editorial inference: the same lepton-aware tagger could be applied to nonresonant dark-shower production, where there is no $Z'$ peak; in that setting the MD-ABCDISCOTEC independence requirement would have to be validated over a wider phase space than the one used here.
  • Editorial inference: the exclusions rest on the two discriminators staying independent inside the low-$\Delta\eta$ signal region; a direct closure test there, using signal injections or inverted selections, would be the cleanest check that the quoted mass ranges are not shifted by residual correlation.
  • Editorial inference: the mild excess at $m_{Z'}=3$ TeV is the most interesting point for future data; if it persists, a tau-enriched dark sector with a large invisible fraction is the model shape it would favor.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. This paper reports a CMS search for resonant production of lepton-enriched semivisible jets in 138 fb−1 of 13 TeV proton-proton collisions. Two benchmark hidden-valley scenarios are considered: SVJℓ, with democratic dark-vector decays through a dark photon, and SVJτ, with dark-pion decays to tau leptons. The analysis uses a Lund-tree GNN tagger based on LUNDNET together with a mass-decorrelated variant of the ABCDiscoTEC neural-network background-estimation method, applied in bins of the dijet transverse mass mT. The observed data are consistent with the background prediction, and 95% CL limits are set on the product σ(Z′)B_dark for the two signal models; the SVJℓ benchmark excludes mediator masses up to 4.7 TeV, and the SVJτ benchmark excludes masses between 1.8 and 3.5 TeV for the quoted parameter choices. The paper claims the first experimental constraints on lepton-enriched semivisible jets.

Significance. If the result is correct, it provides a genuinely new experimental constraint on a class of strongly coupled dark-sector models with leptonic dark-hadron decays, extending earlier fully hadronic semivisible-jet searches. The analysis is technically sophisticated: it introduces a mass-decorrelated ABCD method with distance-correlation and closure losses, validates the background prediction in a high-Δη control region, assigns explicit nonclosure normalization and shape systematics, and provides tabulated results in HEPData. The central exclusion claim is, however, conditional on the assumed benchmark signal models of Refs. [44,45] and on the validity of the background-estimation assumptions in the low-Δη signal region; the main technical risk is that no fully independent closure test is available in the signal region itself.

major comments (2)
  1. [8.1, 8.3, and 9] The central exclusion limits rest on the MD-ABCDISCOTEC background prediction in the low-Δη signal region, but the method is never validated in data in that region. The networks are trained with the closure loss and the three DisCo losses of Eq. (3) on simulated events in the Δη-extended region, which includes the low-Δη SR itself, and the high-Δη validation region has a different background composition (larger QCD fraction, as described in Section 6). The shape systematic is derived from the same low-Δη simulated background on which closure was enforced, with a Poisson uncertainty added to emulate data statistics; this does not cover a possible data-versus-MC difference in the residual D1–D2 correlation. Because the ABCD estimate N_A = N_B N_C/N_D is applied bin-by-bin in mT, a residual correlation in the low-statistics high-mT bins would directly bias the predicted background and shift the exclusion contours in Figs. 12 and 13. I recommend that the manuscript add an independent closure test in a signal-depleted low-Δη region (for example, an mT sideband or an inverted lepton-isolation selection), or, failing that, a quantitative estimate of the maximum bias from residual D1–D2 correlation together with an enlarged shape systematic that covers it.
  2. [8.3 and 9] The quoted distance-correlation values (0.001–0.005) are global values computed on the full simulated training sample, and the goodness-of-fit p-values (0.75 and 0.98) are computed after the fit. Neither demonstrates that D1 and D2 are independent separately in each mT bin or for each background process in the low-Δη SR. The closure loss and the ABCD boundary optimization in Section 8.3 are performed on the same MC sample, so the MC closure is partly a training outcome rather than an independent check. Please report per-bin DisCo values and per-bin closure ratios for the low-Δη SR before the fit, and show the pre-fit SR data and prediction in addition to the post-fit distributions in Figs. 9–11.
minor comments (4)
  1. [7.2] The sentence 'The LUNDNET therefore improves both the speed both of the training and the inference' contains a duplicated 'both'.
  2. [6, Eq. (2)] The definition of I_mini includes the term pT,pileup, but the text does not precisely define how the scalar sum entering this term is computed; please add a definition.
  3. [9] The statement that the statistical uncertainty accounts for 80–90% of the total uncertainty while the systematic contribution is 40–60% is not obviously consistent; please clarify whether these are fractions of the variance or of the standard deviation.
  4. [Abstract and 11] The quoted SVJτ exclusion range of 1.8–3.5 TeV corresponds to a specific benchmark (r_inv = 0.3, Bτ = 0.3, m_dark = 8 GeV); please state this qualification in the abstract or summary to avoid overgeneralization.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the exclusion limits compare observed data to a data-driven ABCD background estimate and to external benchmark signal cross sections, with no fitted parameter renamed as a prediction.

full rationale

The central result is an experimental exclusion: observed yields in the low-Δη signal region are compared with a background estimate N_A = N_B N_C / N_D built from data control regions, and with theoretical σ(Z′) B_dark values taken from the benchmark signal models of Refs. [44,45] and [70]. No parameter is fitted to the observed SR yield and then reinterpreted as a prediction; the MD-ABCDISCOTEC networks are trained on MC, and the ABCD boundaries are chosen using simulated background, but the final SR estimate is computed from observed B, C, and D counts. The closure loss in Eq. (3) encodes the ABCD relation as a training target on simulation, which is a methodological construction rather than a derived prediction; the paper validates the method against data in the high-Δη region and assigns explicit normalization and shape nonclosure systematic uncertainties. The signal models are cited benchmark inputs, not outputs of this paper, and the author overlap in Refs. [44,45,48] does not make the exclusion self-referential because the constraints are explicitly conditional on those models. The shape systematic derived from low-Δη simulated background is a potential validation-coverage concern, but it is an uncertainty-estimation issue, not a circular derivation of the central claim. The derivation chain is therefore self-contained against external benchmarks and observed data.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The analysis introduces no new particles or interactions; the Z', A', dark quarks, and dark hadrons are benchmark model inputs from Refs. [44,45] and are represented as domain assumptions. The free parameters are the benchmark model parameters scanned to map the excluded region, plus analysis thresholds and ML hyperparameters chosen by hand. The most important structural assumption is that the ABCD discriminators are independent for backgrounds in the signal region.

free parameters (8)
  • m_Z' (mediator mass) = scanned 1.5-5 TeV in 0.5 TeV steps
    Sets the resonance pole and signal kinematics; limits are quoted as functions of this parameter.
  • r_inv (invisible dark hadron fraction) = scanned 0.3, 0.5, 0.7
    Controls the fraction of stable dark hadrons; directly drives missing transverse momentum and selection efficiency.
  • Lambda_dark (dark confinement scale) = 5, 10, 20 GeV (SVJ l); 10, 15 GeV (SVJ tau)
    Controls dark QCD coupling and dark hadron multiplicity; varied to map model dependence of limits.
  • m_dark / Lambda_dark = 1.6 (SVJ l); 0.8 and 1.0 (SVJ tau)
    Sets which dark hadrons can decay to SM particles; determines the allowed decay topology for each scenario.
  • B_tau = B(pi_dark -> tau+ tau-) = scanned 0.3, 0.5, 0.7 (SVJ tau only)
    Controls tau enrichment of the jets and affects B_dark; limits show only mild dependence on this parameter.
  • Coupling benchmarks g_q / g_u, g_chi, epsilon_eff = g_q = 0.25; g_chi = 0.4 (SVJ l) or 0.6 (SVJ tau); epsilon_eff saturating EWPT constraints
    Set the Z' production cross section and dark-hadron branching fractions; chosen from Refs. [44,45] and the LHC DM working group, not fitted to data.
  • ABCD region boundaries = (0.58, 0.58) for SVJ l; (0.65, 0.65) or (0.55, 0.55) for SVJ tau multilepton (text/figure discrepancy)
    Chosen by optimizing signal significance and closure figures of merit on simulated data; these thresholds define the signal region and affect the background estimate.
  • MD-ABCDISCOTEC training hyperparameters = constraint thresholds epsilon, damping factors, batch sizes 8192-12288, initial learning rate 0.001
    Tuned to balance the cross-entropy, DisCo, and closure losses; part of the analysis design but not physics parameters.
assumptions (6)
  • domain assumption The SM backgrounds (QCD, t-tbar, W/Z+jets) are modeled by PYTHIA/MadGraph with the stated PDFs and tunes, and their normalizations are taken from NNLO cross sections.
    Section 4.2: The background estimate and ML training rely on these simulations being accurate in the high-pT regime.
  • domain assumption The signal models of Refs. [44,45] (dark QCD with N_c = 3, N_f = 2, a Z' mediator, and prompt dark-hadron decays) describe the actual dark sector.
    Section 2: All limits are conditional on this benchmark model class.
  • domain assumption The two MD-ABCDISCOTEC discriminators D1 and D2 are independent for background in the signal region.
    Section 8: Required for the ABCD relation N_A = N_B N_C / N_D; validated only in the high-Delta-eta control region.
  • standard math The narrow-width approximation for the Z' mediator holds.
    Section 4.1 invoking Ref. [71]; Gamma_Z' / m_Z' is below 5.5-7%, allowing on-shell treatment of the production.
  • domain assumption Dark hadron masses derived from lattice QCD fits to m_dark / Lambda_dark are reliable.
    Section 4.1 invoking Ref. [69]; the dark hadron masses set the signal kinematics and decay thresholds.
  • domain assumption The jet-based triggers are fully efficient for mT > 1.5 TeV, with at least 95% of the plateau efficiency.
    Section 6: The SR requires mT > 1.5 TeV, and simulated events are corrected to match measured trigger efficiency.

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Cite this review

Pith. "Pith review of Search for resonant production of lepton-enriched semivisible jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV." pith.science (2026). https://pith.science/paper/JGX2WG5J

@misc{pith2026260805323,
  author       = {Pith},
  title        = {Pith review of: Search for resonant production of lepton-enriched semivisible jets in proton-proton collisions at $\sqrts$ = 13 TeV},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JGX2WG5J}},
  note         = {Machine review of arXiv:2608.05323}
}
abstract

This search targets the resonant production of lepton-enriched semivisible jets (SVJs) from a strongly coupled dark sector, using 138 fb$^{-1}$ of proton-proton collision data collected with the CMS detector at the CERN LHC at $\sqrt{s}$ = 13 TeV. Two scenarios are investigated: jets enriched in all lepton flavors (SVJ$\ell$ signature) and jets predominantly enriched in tau leptons (SVJ$\tau$ signature). The analysis focuses on final states in which the missing transverse momentum is aligned with jets containing nonisolated leptons. A dual machine-learning strategy is employed, using a graph neural network for jet identification and a fully connected neural network that combines jet- and event-level information to enhance signal sensitivity and background estimation. The signal models assume a heavy Z' mediator with a benchmark coupling of 0.25 to standard model quarks, together with prompt decays of unstable dark hadrons. In the SVJ$\ell$ scenario, mediator masses up to 4.7 TeV are excluded at 95% confidence level, while masses between 1.8 and 3.5 TeV are excluded in the SVJ$\tau$ scenario. These results provide the first experimental constraints on lepton-enriched semivisible jets.

Figures

Figures reproduced from arXiv: 2608.05323 by the authors.

Figure 1
Figure 1. Left: diagram of s-channel production of SVJs. Right: dark hadrons decay modes in the SVJℓ and SVJτ scenarios. particles. The final signature is characterized by SVJs containing hadrons along with pairs of oppositely charged leptons (electrons, muons, and taus) produced by the decay of unstable dark bound states. The lifetime of the dark vector mesons is controlled by the parameter ϵ, and prompt decays are possible … view at source ↗
Figure 2
Figure 2. Distribution of Imini(µ) in the ∆η-extended region after applying all selection require￾ments (except for Imini itself) for simulated background processes and various models of SVJℓ with mdark = 16 GeV (left) and SVJτ with mdark = 8 GeV (right). The dashed vertical lines in￾dicate the selection requirement for isolated leptons. The sum of the background contributions as well as each individual signal process are nor… view at source ↗
Figure 3
Figure 3. Illustration of the Lund tree before and after pruning, and its conversion into a pruned [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Left: LUNDNET jet tagger score for the two highest pT jets in the multilepton cate￾gory (∆η-extended region) for different SVJℓ signal models (with mdark = 16 GeV), simulated backgrounds, and data. The sum of the background contributions, data as well as each individ￾u…
Figure 5
Figure 5. Figure 5: Left: the ROC curves presenting the possible working points of L [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: Sketch of the MD-ABCDISCOTEC background estimation method. On the left, the ABCD plane is shown, defined by the scores of the MD-ABCDISCOTEC neural network. On the right, the effect of mass decorrelation during the network training is illustrated: it results in similar…
Figure 7
Figure 7. Figure 7: Left: evolution of the different components of the loss function in the training of the [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: Left: density distribution of simulated background and SVJ [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]
Figure 9
Figure 9. Figure 9: Comparison of estimated background and observed data in the multilepton low- [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]
Figure 10
Figure 10. Figure 10: Comparison of estimated background and observed data in the 0-lepton low- [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]
Figure 11
Figure 11. Figure 11: Comparison of estimated background and observed data in the multilepton low- [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: The 95% CL upper limits on σZ ′Bdark for the SVJℓ model as functions of mZ ′ , for rinv values of 0.3 (upper), 0.5 (middle), and 0.7 (lower), and mdark = 16 (left) and 32 GeV (right). The red solid line labeled “Theory” represents the product of the nominal Z′ cross s…
Figure 13
Figure 13. Figure 13: The 95% CLs upper limits on σZ ′Bdark for the SVJτ model as functions of mZ ′ , for rinv = 0.3 (Bτ = 0.3) (upper); rinv = 0.5 (Bτ = 0.3) (upper middle); rinv = 0.7 (Bτ = 0.3) (lower middle); rinv = 0.3 (Bτ = 0.7) (lower); and mdark = 8 (left) and 12 GeV (right). The r…

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.