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arxiv: 2510.17675 · v2 · pith:CVKCBHRQnew · submitted 2025-10-20 · ✦ hep-ph

Probing the Higgs Portal to a Strongly-Interacting Dark Sector at the FCC-ee

Cesare Cazzaniga , Annapaola de Cosa , Felix Kahlhoefer , Andrea S. Maria , Roberto Seidita , Emre Sitti This is my paper

Pith reviewed 2026-05-25 08:10 UTC · model grok-4.3

classification ✦ hep-ph
keywords Higgs portaldark sectorsemi-visible jetsFCC-eegraph neural networkexotic decaysconfining dark sectormachine learning
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The pith

Graph neural networks can identify semi-visible jets from Higgs decays into dark quarks at the FCC-ee, reaching per-mille sensitivity on exotic branching ratios.

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

The paper explores how electron-positron collisions at the Future Circular Collider can reveal signatures of a confining dark sector coupled to the Standard Model through the Higgs boson. Dark quarks produced in Higgs decays form semi-visible jets that mix visible and invisible particles. When the invisible part is large, missing energy discriminates the signal, but for smaller invisible fractions the signals look more like ordinary events, so the authors train a graph neural network to tag the jets based on their internal structure and particle content. This approach allows the experiment to set strong limits on the fraction of Higgs decays going into the dark sector. A sympathetic reader would care because it turns a future collider into a tool for testing models of dark matter that involve strong dynamics.

Core claim

The proposed strategy of using kinematic features for high invisible fractions and a graph neural network jet tagger for lower fractions can effectively probe a wide parameter space for confining dark sector models, constraining the Higgs boson exotic branching ratios into dark quarks at the permille level.

What carries the argument

A graph neural network jet tagger that exploits differences in substructure, lepton, and photon content of semi-visible jets compared to Standard Model backgrounds.

If this is right

  • It enables sensitivity to dark sectors with varying invisible particle fractions.
  • The method improves discovery prospects for Higgs-induced semi-visible jets.
  • It can constrain a variety of signatures in Higgs portal dark sector models.
  • Exotic branching ratios can be limited to the 0.001 level at the FCC-ee.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the tagging works, the same GNN approach might be adapted for searches at hadron colliders like the LHC where backgrounds are higher.
  • Success would encourage development of dedicated triggers for semi-visible jet events at future e+e- machines.
  • The results assume specific dark sector parameters, so varying the confinement scale could lead to different jet properties worth exploring further.

Load-bearing premise

The dark sector is confining and produces semi-visible jets whose substructure and lepton or photon content differ enough from Standard Model backgrounds that a graph neural network trained on simulation can tag them reliably at low invisible fractions.

What would settle it

If the graph neural network, when applied to real FCC-ee data, cannot achieve the simulated discrimination power between semi-visible jets and Standard Model backgrounds, the projected constraints would not hold.

Figures

Figures reproduced from arXiv: 2510.17675 by Andrea S. Maria, Annapaola de Cosa, Cesare Cazzaniga, Emre Sitti, Felix Kahlhoefer, Roberto Seidita.

Figure 1
Figure 1. Figure 1: Sketch of the process studied in this work: [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Signal-wise AUC scores in the low-rinv regime (left) and in the high-rinv regime (right). pseudo-jets. In the low-rinv scenario, the average frac￾tion of muons over all pseudo-jets in the graph tends to increase with rinv and decrease with Λ, see Appendix D. This trend in the muon energy fraction reproduces the observed AUC performances, suggesting the impor￾tance of this feature in discriminating signal f… view at source ↗
Figure 3
Figure 3. Figure 3: Expected sensitivities in the high rinv regime without (left) and with (right) the GNN tagger as a function of Λ and pη for fixed pv = 0.5 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Expected sensitivities in the low rinv regime without (left) and with (right) the GNN tagger as a function of (Λ, rinv) and rinv. 7 Conclusions In this work, we developed and tested a dedicated search for SVJs at the FCC-ee operating at √ s = 240 GeV, with production mediated by the Higgs boson and lever￾aging the clean Z → ℓ +ℓ − recoil topology. We con￾structed a benchmark program that (i) maps the effec… view at source ↗
Figure 5
Figure 5. Figure 5: rinv values obtained with simulation data chang￾ing pv and pη for different Λ values [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Signal efficiency in (Λ, rinv) for the low-rinv sce￾nario. Appendix B: Signal Efficiencies For the selections in the low and high rinv regimes, de￾fined in Tables 4, the final signal selection efficiency is studied in the parameter space of the models. In the low-rinv regime, the signal efficiency is shown in the top panel of [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Signal efficiencies in the parameter space for the [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: High-rinv AUCs for the remaining parameter space [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Averaged input features for all nodes of all [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Sensitivity limits without (a) and with (b) GNN [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
read the original abstract

This work explores exotic signatures from confining dark sectors that may arise in the $e^+e^-$ collision mode at the Future Circular Collider. Assuming the Higgs boson mediates the interaction between the Standard Model and the dark sector, dark quarks can be produced in $e^+e^-$ collisions. The ensuing strong dynamics may lead to semi-visible jet final states, containing both visible and invisible particles. We investigate semi-visible jets with different fractions of invisible states, and enriched in leptons and photons. When the invisible component is large, selections based on kinematic features, such as the missing energy in the event, already provide good signal-to-background discrimination. For smaller invisible fractions, the reduced missing energy makes these signals more similar to Standard Model events, and we therefore employ a graph neural network jet tagger exploiting differences in jet substructure. This machine learning strategy improves sensitivity and enhances the discovery prospects of Higgs boson-induced semi-visible jets at the Future Circular Collider. Our results show that the proposed strategy can effectively probe a wide parameter space for the models considered, and a variety of signatures, constraining the Higgs boson exotic branching ratios into dark quarks at the permille-level.

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.

Referee Report

1 major / 0 minor

Summary. The manuscript explores exotic Higgs decays to dark quarks in a confining dark sector at the FCC-ee, leading to semi-visible jets. It shows that for high invisible fractions, missing energy selections work well, but for low invisible fractions, a graph neural network jet tagger exploiting substructure differences improves sensitivity, allowing constraints on exotic branching ratios at the permille level.

Significance. If the results hold, this provides a concrete strategy to search for strongly interacting dark sectors at future e+e- colliders using ML techniques, which could be significant for the field as it addresses the challenging low-MET regime. The use of GNN for jet tagging in this context is a positive aspect.

major comments (1)
  1. [Abstract] Abstract: The claim that the GNN strategy constrains exotic Higgs branching ratios at the permille level rests on the tagger delivering usable discrimination for small invisible fractions; however, the manuscript provides no data-driven closure tests, control-region validation, or variations of dark-sector hadronization parameters to quantify how the quoted efficiencies and sensitivities degrade under realistic modeling uncertainties in jet substructure.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for the positive assessment of the work's significance and for highlighting the need to better quantify modeling uncertainties in the GNN tagger performance. We address the major comment below and will incorporate revisions to strengthen the robustness discussion.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the GNN strategy constrains exotic Higgs branching ratios at the permille level rests on the tagger delivering usable discrimination for small invisible fractions; however, the manuscript provides no data-driven closure tests, control-region validation, or variations of dark-sector hadronization parameters to quantify how the quoted efficiencies and sensitivities degrade under realistic modeling uncertainties in jet substructure.

    Authors: We agree that additional quantification of modeling uncertainties would strengthen the presentation. As this is a prospective study for the future FCC-ee collider, data-driven closure tests and control-region validations using real collision data are not possible. However, we will add a new subsection (Section 4.3) performing variations of key dark-sector parameters, including the confinement scale, dark hadronization models (e.g., Lund string vs. cluster), and invisible fraction assumptions, to assess the stability of the GNN efficiencies and resulting branching ratio limits. These studies will be used to attach systematic uncertainty bands to the quoted sensitivities. We will also revise the abstract to include a brief caveat noting that the permille-level projections assume the baseline simulation setup and are subject to dark-sector modeling uncertainties. revision: partial

standing simulated objections not resolved
  • Data-driven closure tests and control-region validations cannot be performed, as the analysis is a Monte Carlo projection study for a future collider with no existing data.

Circularity Check

0 steps flagged

No circularity: sensitivity derived from independent Monte Carlo and ML simulation chain

full rationale

The paper's central results are obtained by generating simulated events for signal and background, training a graph neural network on those samples, and extracting efficiencies and limits from the trained tagger performance. No equation or parameter is defined in terms of the final sensitivity; the GNN training and kinematic selections are standard external tools whose outputs are not forced to reproduce the input assumptions. No self-citation chain is invoked to justify uniqueness or an ansatz. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 1 invented entities

The analysis rests on standard collider simulation assumptions plus model-specific choices for the dark sector; no machine-checked proofs or external data anchors are mentioned.

free parameters (2)
  • invisible fraction
    Varied across scenarios to test different signal topologies; directly affects missing-energy and jet-substructure observables.
  • dark quark mass and confinement scale
    Set by hand to define the dark hadron spectrum and jet properties in the Monte Carlo.
axioms (2)
  • domain assumption Higgs boson mediates all interactions between the Standard Model and the dark sector
    Stated in the abstract as the portal assumption that enables dark quark production in e+e- collisions.
  • domain assumption Dark sector is confining and produces semi-visible jets with distinct substructure
    Required for the signal definition and for the GNN to have discriminating power.
invented entities (1)
  • dark quarks no independent evidence
    purpose: Constituent particles of the confining dark sector that are produced via the Higgs portal
    Postulated new particles whose strong dynamics generate the semi-visible jet signature; no independent evidence provided.

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Forward citations

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Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages · cited by 1 Pith paper · 3 internal anchors

  1. [1]

    Bertone, D

    G. Bertone, D. Hooper, J. Silk, Phys. Rept.405, 279 (2005). URLhttps://doi.org/10.1016/j.physrep. 2004.08.031

    work page doi:10.1016/j.physrep 2005

  2. [2]

    Aghanim, Y

    N. Aghanim, Y. Akrami, M. Ashdown, J. Aumont, et al., Astronomy & Astrophysics641, A6 (2020). URLhttp: //dx.doi.org/10.1051/0004-6361/201833910

    work page doi:10.1051/0004-6361/201833910 2020

  3. [3]

    Strassler, K.M

    M.J. Strassler, K.M. Zurek, Phys. Lett. B651(5–6), 374 (2007). URLhttp://dx.doi.org/10.1016/j.physletb. 2007.06.055

    work page doi:10.1016/j.physletb 2007

  4. [4]

    Bernreuther, F

    E. Bernreuther, F. Kahlhoefer, M. Krämer, P. Tunney, JHEP01(1), 162 (2020). URLhttp://dx.doi.org/10. 1007/JHEP01(2020)162

    work page 2020

  5. [5]

    Beauchesne, E

    H. Beauchesne, E. Bertuzzo, G. Grilli Di Cortona, JHEP04, 118 (2019). URLhttps://doi.org/10.1007/ JHEP04(2019)118

    work page 2019

  6. [6]

    Craig, A

    N. Craig, A. Katz, M. Strassler, R. Sundrum, JHEP 07, 105 (2015). URLhttps://doi.org/10.1007/ JHEP07(2015)105

    work page 2015

  7. [7]

    Cohen, M

    T. Cohen, M. Lisanti, H.K. Lou, Phys. Rev. Lett. 115(17), 171804 (2015). URLhttps://link.aps.org/ doi/10.1103/PhysRevLett.115.171804

    work page doi:10.1103/physrevlett.115.171804 2015

  8. [8]

    Cohen, M

    T. Cohen, M. Lisanti, H.K. Lou, S. Mishra-Sharma, JHEP11, 196 (2017). URLhttps://doi.org/10.1007/ JHEP11(2017)196

    work page 2017

  9. [9]

    Beauchesne, E

    H. Beauchesne, E. Bertuzzo, G. Grilli Di Cortona, Z. Tabrizi, JHEP08, 030 (2018). URLhttps://doi. org/10.1007/JHEP08(2018)030

    work page doi:10.1007/jhep08(2018)030 2018

  10. [10]

    Beauchesne, G

    H. Beauchesne, G. Grilli di Cortona, JHEP02, 196 (2020). URLhttps://doi.org/10.1007/JHEP02(2020) 196

    work page doi:10.1007/jhep02(2020 2020

  11. [11]

    Knapen, J

    S. Knapen, J. Shelton, D. Xu, Phys. Rev. D103(11), 115013 (2021). URLhttp://dx.doi.org/10.1103/ PhysRevD.103.115013

    work page 2021

  12. [12]

    Cazzaniga, A

    C. Cazzaniga, A. Russo, E. Sitti, A. de Cosa, Eur. Phys. J. C84(11), 1223 (2024). URLhttps://doi.org/10. 1140/epjc/s10052-024-13613-9

    work page 2024

  13. [15]

    Azzurri, G

    P. Azzurri, G. Bernardi, S. Braibant, D. d’Enterria, J. Eysermans, P. Janot, A. Li, E. Perez, Eur. Phys. J. Plus137(1), 23 (2022). URLhttps://doi.org/10.1140/ epjp/s13360-021-02202-4

    work page 2022

  14. [16]

    Higgs measurement at e+e- circular colliders

    M. Ruan, Nucl. Part. Phys. Proc.273-275, 857 (2016). URLhttps://doi.org/10.48550/arXiv.1411.5606

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1411.5606 2016

  15. [17]

    Antusch, et al., Eur

    S. Antusch, et al., Eur. Phys. J. Plus136(11), 1163 (2021). DOI 10.1140/epjp/s13360-021-01875-1. URL https://link.springer.com/article/10.1140/epjp/ s13360-021-01875-1

    work page doi:10.1140/epjp/s13360-021-01875-1 2021

  16. [19]

    Berlin, N

    A. Berlin, N. Blinov, S. Gori, P. Schuster, N. Toro, Phys. Rev. D97(5), 055033 (2018). URLhttps://doi.org/ 10.1103/PhysRevD.97.055033

    work page doi:10.1103/physrevd.97.055033 2018

  17. [20]

    Ilten, Y

    P. Ilten, Y. Soreq, M. Williams, W. Xue, Journal of High Energy Physics2018(6) (2018). URLhttp://dx.doi. org/10.1007/JHEP06(2018)004

    work page doi:10.1007/jhep06(2018)004 2018

  18. [21]

    FCCAnalyses: Framework for physics analysis at FCC (2025)

    FCC Collaboration. FCCAnalyses: Framework for physics analysis at FCC (2025). URLhttps://github. com/HEP-FCC/FCCAnalyses. Accessed: 20 March 2025

    work page 2025

  19. [22]

    A comprehensive guide to the physics and usage of PYTHIA 8.3

    C. Bierlich, et al., SciPost Physics Codebases (2022). URLhttps://doi.org/10.48550/arXiv.2203.11601

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2203.11601 2022

  20. [24]

    Carloni, T

    L. Carloni, T. Sjostrand, JHEP09, 105 (2010). URL https://doi.org/10.1007/JHEP09%282010%29105

    work page doi:10.1007/jhep09 2010

  21. [25]

    de Favereau, C

    J. de Favereau, C. Delaere, P. Demin, A. Giammanco, et al., JHEP02, 057 (2014). URLhttps://doi.org/10. 1007/JHEP02(2014)057

    work page 2014

  22. [26]

    Abbrescia, et al., (2025)

    M. Abbrescia, et al., (2025). URLhttps://doi.org/10. 48550/arXiv.2502.21223

    work page arXiv 2025

  23. [27]

    Cacciari, G.P

    M. Cacciari, G.P. Salam, G. Soyez, JHEP04, 063 (2008). URLhttps://doi.org/10.1088/1126-6708/ 2008/04/063

    work page doi:10.1088/1126-6708/ 2008

  24. [29]

    Benedikt, et al., (2025)

    M. Benedikt, et al., (2025). URLhttps://doi.org/10. 17181/CERN.9DKX.TDH9

    work page 2025

  25. [30]

    Albouy, et al., Eur

    G. Albouy, et al., Eur. Phys. J. C82(12), 1132 (2022). URLhttp://dx.doi.org/10.1140/epjc/ s10052-022-11048-8

    work page doi:10.1140/epjc/ 2022

  26. [31]

    Asymptotic formulae for likelihood-based tests of new physics

    G. Cowan, K. Cranmer, E. Gross, O. Vitells, The Eu- ropean Physical Journal C71(2) (2011). URLhttp: //dx.doi.org/10.1140/epjc/s10052-011-1554-0

    work page internal anchor Pith review doi:10.1140/epjc/s10052-011-1554-0 2011

  27. [32]

    Dreyer, H

    F.A. Dreyer, H. Qu, JHEP03, 052 (2021). URLhttps: //doi.org/10.1007/JHEP03(2021)052

    work page doi:10.1007/jhep03(2021)052 2021

  28. [33]

    Dreyer, G.P

    F.A. Dreyer, G.P. Salam, G. Soyez, JHEP12, 064 (2018). URLhttps://doi.org/10.1007/JHEP12%282018%29064

    work page doi:10.1007/jhep12 2018

  29. [34]

    The CMS Collaboration, Search for s-channel production of lepton-enriched semivisible jets in proton-proton colli- sions at 13 TeV. Tech. rep., CERN, Geneva (2025). URL https://cds.cern.ch/record/2940795

    work page arXiv 2025

  30. [35]

    Gallicchio, J

    J. Gallicchio, J. Huth, M. Kagan, M.D. Schwartz, K. Black, B. Tweedie, JHEP04, 069 (2011). URL https://doi.org/10.1007/JHEP04%282011%29069

    work page doi:10.1007/jhep04 2011

  31. [36]

    Junk, Nucl

    T. Junk, Nucl. Instrum. Meth. A434, 435 (1999). URL https://doi.org/10.1016/S0168-9002%2899%2900498-2

    work page doi:10.1016/s0168-9002 1999

  32. [37]

    A.L. Read, J. Phys. G28, 2693 (2002). URL https://iopscience.iop.org/article/10.1088/ 0954-3899/28/10/313

    work page 2002

  33. [38]

    Hayrapetyan, et al., Comput

    A. Hayrapetyan, et al., Comput. Softw. Big Sci. 8(1), 19 (2024). URLhttps://doi.org/10.1007/ s41781-024-00121-4

    work page 2024

  34. [39]

    URL https://cds.cern.ch/record/2800581

    A.e.a.Tumasyan,Phys.Lett.B816,136188(2021). URL https://cds.cern.ch/record/2800581

    work page arXiv 2021