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CMS finds no excess of boosted nonprompt dark matter in 138 fb−¹ of 13 TeV data and sets first limits on a four-prong large-radius-jet plus missing-momentum signature.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

T0 review · grok-4.5

2026-07-14 07:37 UTC pith:YQ6WFXTR

load-bearing objection Solid first CMS search for boosted nonprompt DM pairs; null result with data-driven backgrounds and a reusable GNN, limited mainly by large tagger SFs.

arxiv 2607.11016 v1 pith:YQ6WFXTR submitted 2026-07-13 hep-ex

Search for dark matter in a signature with a four-prong large-radius jet in proton-proton collisions at sqrt{s} = 13 TeV

classification hep-ex PACS 13.85.Rm14.80.-j95.35.+d
keywords dark matterlong-lived particleslarge-radius jetsjet substructuregraph neural networkmissing transverse momentumCMSLHC
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper reports the first search for a pair of long-lived dark-sector particles that each decay into a stable dark-matter particle and a light boson that decays to quarks, all produced via a heavy mediator in association with an energetic initial-state-radiation jet. Because of the large Lorentz boost, the four quarks from the two light bosons are reconstructed as a single large-radius jet whose four-prong, potentially displaced substructure is identified by a graph-neural-network tagger. Using the full 2016–2018 CMS data set of 138 fb−¹ at 13 TeV, the analysis examines the missing-transverse-momentum spectrum after estimating the dominant multijet, W+jets and Z+jets backgrounds from dedicated control regions. No significant excess above the Standard Model expectation is observed. The result therefore places the first 95 % confidence-level upper limits on the signal strength as functions of the mediator mass or the coupling that controls the lifetime of the long-lived particles, thereby opening a previously unexplored experimental window on nonprompt dark matter in the boosted topology.

Core claim

No significant excess over the Standard Model background is observed in the missing-transverse-momentum spectrum of events that contain a four-prong large-radius jet tagged by a graph neural network. Consequently the analysis sets the first 95 % CL upper limits on the production of a pair of nonprompt dark-matter candidates in the Lorentz-boosted topology, expressed as functions of either the heavy mediator mass or the coupling that sets the lifetime of the intermediate dark-sector particles.

What carries the argument

A graph-neural-network jet tagger trained on particle-flow constituents and secondary-vertex information, which selects large-radius jets whose multiprong, potentially displaced substructure matches the four-quark decay of a boosted pair of light dark mediators; the tagger score defines the signal region and the orthogonal control regions used for data-driven background estimation.

Load-bearing premise

The large (50–70 %) scale-factor uncertainties that correct the tagger efficiency from simulation to data for highly collimated, highly displaced four-prong jets correctly capture residual reconstruction differences; if those corrections are wrong the extracted signal limits shift substantially.

What would settle it

A statistically significant excess of events in the high-missing-transverse-momentum tail of the signal region relative to the background prediction obtained from the simultaneous control-region fit, after the graph-neural-network tagger requirement has been applied.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The parameter space of vector/axial-vector mediators that produce long-lived dark-sector fermions decaying to stable dark matter plus a light quark-pair boson is now directly constrained for the first time in the boosted four-prong topology.
  • Future analyses can reuse the same GNN-plus-control-region strategy for other multiprong displaced signatures once higher-luminosity data become available.
  • The limits already exclude the benchmark points with the largest theoretical cross sections for the couplings and masses listed in the paper.
  • The result demonstrates that a single large-radius jet plus missing momentum is a viable experimental handle for nonprompt dark matter even when the intermediate particles have millimetre-to-metre lifetimes.

Where Pith is reading between the lines

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

  • Because the tagger performance degrades for the most displaced and heaviest signals, dedicated lifetime-binned or secondary-vertex-aware taggers could recover sensitivity in the long-lifetime regime that this analysis leaves comparatively unconstrained.
  • The same boosted four-prong topology could be re-examined with Run-3 data and improved secondary-vertex inputs to test whether residual data–simulation discrepancies in the Lund-plane reweighting shrink, thereby tightening the dominant systematic.
  • If a future excess appears in the same final state, the control-region transfer-factor method already provides a ready-made background model for a discovery claim.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 5 minor

Summary. This CMS paper presents a search for nonprompt dark-matter production in the Lorentz-boosted topology using 138 fb^{-1} of 13 TeV pp data. A heavy vector/axial-vector mediator Y1 produces a pair of long-lived dark fermions χ2 that each decay to a stable DM particle χ1 plus a light scalar Y0 (m_Y0 = 1 GeV) decaying to quarks; the four quarks are reconstructed as a single four-prong AK8 jet accompanied by large pTmiss and an ISR jet. Events are selected with a GNN jet-substructure tagger that incorporates secondary-vertex information, and the three dominant backgrounds (QCD multijet, W+jets, Z o u u+jets) are estimated from data via transfer factors in dedicated control regions that are fitted simultaneously with the signal region. No significant excess is observed; 95% CL upper limits are set on the signal strength as functions of the Y1 mass or the coupling g_χ2χ1Y0 that controls the χ2 lifetime. The analysis is presented as the first search for this signature in the boosted regime.

Significance. The result is a solid, first-of-its-kind experimental constraint on a previously unexplored boosted displaced DM topology. The analysis follows standard CMS Run-2 practice: data-driven transfer-factor background estimates constrained in situ by simultaneous CR+SR fits, a full suite of experimental systematics, and CLs limits with the asymptotic approximation. The GNN tagger that exploits both multiprong substructure and secondary vertices, together with the first extension of Lund-jet-plane reweighting to displaced four-prong jets, constitutes a useful technical advance even though the associated 50–70% efficiency uncertainties dominate the signal systematic. The null result and the reported limits remain robust under the stated systematics; the paper therefore supplies a concrete, reproducible exclusion of the tested benchmark points and a clear path for future improvements.

minor comments (5)
  1. [Section 6 / Table 2] Section 6 and Table 2: the 50–70% GNN scale-factor uncertainties are large and dominate the signal systematic. A short additional sentence clarifying how residual lifetime dependence was checked (or why a more differential uncertainty is not assigned) would help readers assess the conservatism of the assignment.
  2. [Figure 2] Figure 2 caption and text: the data/simulation discrepancy in the GNN score is correctly attributed to imperfect QCD modelling and is irrelevant because the QCD yield is taken from data; a one-line reminder that the tagger is used only for region definition (not for absolute rate) would make this even clearer.
  3. [Table 1] Table 1: the theoretical cross sections are quoted without PDF or scale uncertainties. Even a brief statement that these are LO MADGRAPH values and that the limits are reported on μ = σ/σ_theory would remove any ambiguity.
  4. [Section 4] Section 4: the precise definition of the secondary-vertex features fed to the interaction network is only sketched. A short list of the SV observables (or a reference to the training paper) would improve reproducibility.
  5. [Figure 3] Figure 3: the pre-fit signal overlays in the SR are useful, but the vertical scale of the pull panels makes small coherent residuals hard to judge; a linear pull range of ±2 or ±3 would be more informative.

Circularity Check

0 steps flagged

No circularity: standard data-driven experimental search with in-situ CR constraints and simulation-based signal efficiencies.

full rationale

This is a conventional CMS null-result search. The signal model (masses, couplings, lifetimes) is an external benchmark; the GNN tagger is trained on independent simulation and applied as a selection cut; dominant backgrounds (QCD, W+jets, Z+jets) have free normalizations and transfer factors constrained simultaneously from orthogonal data control regions (Eqs. 1–2, Sec. 5); subdominant backgrounds come from MC; the pTmiss spectrum is fitted once with the profile likelihood (Sec. 7) to extract CLs limits on µ. No parameter is fitted to data and then re-used as a “prediction,” no uniqueness theorem is imported from the authors, and self-citations are only to standard detector/performance papers or the LJP method (used solely for a scale-factor uncertainty). The large (50–70 %) tagger SF uncertainties weaken the expected limits but do not force the observed null result by construction. The derivation chain is therefore self-contained against external data and contains no circular step.

Axiom & Free-Parameter Ledger

3 free parameters · 3 axioms · 1 invented entities

Experimental search resting on a simplified dark-sector model whose free parameters are scanned rather than fitted to the data. Background estimation is data-driven; the only large free parameters that affect the central claim are the tagger scale-factor uncertainties and the model-parameter choices that define the signal benchmarks.

free parameters (3)
  • g_χ2χ1Y0 coupling (lifetime proxy) = scanned, not fitted
    Scanned over three discrete values (10^{-7}, 10^{-8}, 5×10^{-9}) that set the χ₂ proper decay length; limits are reported as a function of this coupling.
  • Y1 mediator mass = scanned
    Scanned over 100, 300, 500 GeV (and fixed 300 GeV for the coupling scan); defines the boost and opening angle of the four-quark system.
  • GNN tagger efficiency scale factors = 0.89–1.01 ± 50–70%
    Derived via Lund-jet-plane reweighting; absolute values ~0.9–1.0 with 50–70% uncertainties that dominate the signal systematic (Table 2).
axioms (3)
  • domain assumption The simplified dark-sector model of Buchmueller et al. (vector/axial-vector mediator Y1, long-lived χ₂ → χ₁ Y0, Y0 → qq with m_Y0 = 1 GeV) correctly captures the relevant collider phenomenology.
    Section 1 and Table 1; all signal samples and limit interpretations rest on this model.
  • domain assumption Transfer factors extracted from simulation correctly relate the QCD, W+jets and Z+jets yields between control regions and the signal region in each pTmiss bin.
    Section 5; the data-driven background estimate is only as good as the TF modeling.
  • ad hoc to paper Lund-jet-plane reweighting, previously validated for prompt multiprong jets, remains valid (within the assigned 50–70% uncertainty) for displaced four-prong jets.
    Section 6; first application of the method to displaced topologies; residual limitations are covered by large uncertainties.
invented entities (1)
  • Heavy vector/axial-vector mediator Y1 and long-lived dark fermion χ₂ decaying to stable DM χ₁ + light scalar Y0 no independent evidence
    purpose: Defines the signal process that produces the four-prong jet + MET signature.
    Taken from the simplified-model literature (Buchmueller et al.); not invented here, but the paper’s interpretation is entirely in terms of these particles.

pith-pipeline@v1.1.0-grok45 · 41800 in / 2815 out tokens · 25386 ms · 2026-07-14T07:37:33.490519+00:00 · methodology

0 comments
read the original abstract

A search for a pair of nonprompt dark matter (DM) candidates produced in association with an initial-state radiation jet, in a signature containing a four-prong large-radius jet, is presented. The signal model contains a heavy vector or axial-vector mediator, which produces long-lived dark-sector particles that decay to a stable DM particle and a light boson, which decays to quarks. The analysis is based on data collected in the years 2016$-$2018 with the CMS detector at the LHC in proton-proton collisions at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Signal candidates feature large-radius jets, which are identified using a jet substructure tagger based on a graph neural network. The large-radius jet aims to reconstruct the decay of light DM mediators into four quarks, which are produced in association with two stable DM particles. The standard model background contributions are estimated from data using dedicated control regions. The missing transverse momentum spectrum is probed for a potential signal over the expected background. No significant excess over the standard model expectation is observed. Upper limits at 95% confidence level are set on the signal strength as functions of either the mediator mass or the relevant coupling. This is the first search for a pair of nonprompt DM candidates in the Lorentz-boosted topology, characterized by a large-radius jet and large missing transverse momentum.

Figures

Figures reproduced from arXiv: 2607.11016 by CMS Collaboration.

Figure 1
Figure 1. Figure 1: Representative Feynman diagrams of the signal process. The mediator Y [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of sGNN in the SR before imposing any requirement on sGNN for the range of values considered in this analysis. The data are shown as black markers with vertical bars indicating the statistical uncertainty. Signal processes are shown as solid lines, while the total background, corresponding to the sum of all considered background contributions estimated from simulation, is represented by the fi… view at source ↗
Figure 3
Figure 3. Figure 3: Postfit p miss T distributions in the SR (upper left), QCD CR (upper right), W + jets CR (lower left), and postfit distribution of the magnitude of the hadronic recoil in the Z + jets CR (lower right). The data are shown as black markers with vertical bars indicating the statistical uncertainty. The SM expectation is shown as stacked histograms. In the SR, the prefit expected signal contributions are displ… view at source ↗
Figure 4
Figure 4. Figure 4: Upper limits at 95% CL on the signal strength for different scenarios of the coupling [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

42 extracted references · 1 canonical work pages

  1. [1]

    Dark matter benchmark models for early LHC Run-2 searches: Report of the ATLAS/CMS dark matter forum

    D. Abercrombie et al., “Dark matter benchmark models for early LHC Run-2 searches: Report of the ATLAS/CMS dark matter forum”,Phys. Dark Univ.27(2020) 100371, doi:10.1016/j.dark.2019.100371,arXiv:1507.00966

  2. [2]

    Simplified models for displaced dark matter signatures

    O. Buchmueller et al., “Simplified models for displaced dark matter signatures”,JHEP 09(2017) 076,doi:10.1007/JHEP09(2017)076,arXiv:1704.06515

  3. [3]

    HEPData record for this analysis, 2026.doi:10.17182/hepdata.169917

  4. [4]

    The CMS experiment at the CERN LHC

    CMS Collaboration, “The CMS experiment at the CERN LHC”,JINST3(2008) S08004, doi:10.1088/1748-0221/3/08/S08004

  5. [5]

    Development of the CMS detector for the CERN LHC Run 3

    CMS Collaboration, “Development of the CMS detector for the CERN LHC Run 3”, JINST19(2024) P05064,doi:10.1088/1748-0221/19/05/P05064, arXiv:2309.05466

  6. [6]

    Performance of the CMS Level-1 trigger in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at √s=13 TeV”,JINST15(2020) P10017, doi:10.1088/1748-0221/15/10/P10017,arXiv:2006.10165

  7. [7]

    The CMS trigger system

    CMS Collaboration, “The CMS trigger system”,JINST12(2017) P01020, doi:10.1088/1748-0221/12/01/P01020,arXiv:1609.02366

  8. [8]

    Performance of the CMS high-level trigger during LHC Run 2

    CMS Collaboration, “Performance of the CMS high-level trigger during LHC Run 2”, JINST19(2024) P11021,doi:10.1088/1748-0221/19/11/P11021, arXiv:2410.17038

  9. [9]

    Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC

    CMS Collaboration, “Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC”,JINST16(2021) P05014, doi:10.1088/1748-0221/16/05/P05014,arXiv:2012.06888

  10. [10]

    Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV”,JINST13(2018) P06015, doi:10.1088/1748-0221/13/06/P06015,arXiv:1804.04528

  11. [11]

    Description and performance of track and primary-vertex reconstruction with the CMS tracker

    CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,JINST9(2014) P10009, doi:10.1088/1748-0221/9/10/P10009,arXiv:1405.6569

  12. [12]

    GEANT4—a simulation toolkit

    GEANT4 Collaboration, “GEANT4—a simulation toolkit”,Nucl. Instrum. Meth. A506 (2003) 250,doi:10.1016/S0168-9002(03)01368-8

  13. [13]

    The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

    J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,JHEP07 (2014) 079,doi:10.1007/JHEP07(2014)079,arXiv:1405.0301

  14. [14]

    A new method for combining NLO QCD with shower Monte Carlo algorithms

    P . Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,JHEP11(2004) 040,doi:10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146

  15. [15]

    A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction

    S. Frixione, P . Nason, and G. Ridolfi, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,JHEP09(2007) 126, doi:10.1088/1126-6708/2007/09/126,arXiv:0707.3088. 14

  16. [16]

    Matching NLO QCD computations with parton shower simulations: the POWHEG method

    S. Frixione, P . Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,JHEP11(2007) 070, doi:10.1088/1126-6708/2007/11/070,arXiv:0709.2092

  17. [17]

    NLO single-top production matched with shower in POWHEG: s- and t-channel contributions

    S. Alioli, P . Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in POWHEG: s- and t-channel contributions”,JHEP09(2009) 111, doi:10.1088/1126-6708/2009/09/111,arXiv:0907.4076. [Erratum: doi:10.1007/JHEP02(2010)011]

  18. [18]

    A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX

    S. Alioli, P . Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,JHEP06(2010) 043, doi:10.1007/JHEP06(2010)043,arXiv:1002.2581

  19. [19]

    Top-pair production and decay at NLO matched with parton showers

    J. M. Campbell, R. K. Ellis, P . Nason, and E. Re, “Top-pair production and decay at NLO matched with parton showers”,JHEP04(2015) 114, doi:10.1007/JHEP04(2015)114,arXiv:1412.1828

  20. [20]

    Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations

    P . Artoisenet, R. Frederix, O. Mattelaer, and R. Rietkerk, “Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations”,JHEP03(2013) 015, doi:10.1007/JHEP03(2013)015,arXiv:1212.3460

  21. [21]

    Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements

    CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,Eur. Phys. J. C80(2020) 4, doi:10.1140/epjc/s10052-019-7499-4,arXiv:1903.12179

  22. [22]

    Parton distributions from high-precision collider data

    NNPDF Collaboration, “Parton distributions from high-precision collider data”,Eur. Phys. J. C77(2017) 663,doi:10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428

  23. [23]

    Particle-flow reconstruction and global event description with the CMS detector

    CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,JINST12(2017) P10003,doi:10.1088/1748-0221/12/10/P10003, arXiv:1706.04965

  24. [24]

    The anti-kT jet clustering algorithm

    M. Cacciari, G. P . Salam, and G. Soyez, “The anti-kT jet clustering algorithm”,JHEP04 (2008) 063,doi:10.1088/1126-6708/2008/04/063,arXiv:0802.1189

  25. [25]

    FastJet user manual

    M. Cacciari, G. P . Salam, and G. Soyez, “FastJet user manual”,Eur. Phys. J. C72(2012) 1896,doi:10.1140/epjc/s10052-012-1896-2,arXiv:1111.6097

  26. [26]

    Pileup per particle identification

    D. Bertolini, P . Harris, M. Low, and N. Tran, “Pileup per particle identification”,JHEP10 (2014) 059,doi:10.1007/JHEP10(2014)059,arXiv:1407.6013

  27. [27]

    Pileup mitigation at CMS in 13 TeV data

    CMS Collaboration, “Pileup mitigation at CMS in 13 TeV data”,JINST15(2020) P09018, doi:10.1088/1748-0221/15/09/p09018,arXiv:2003.00503

  28. [28]

    Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV

    CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,JINST12(2017) P02014, doi:10.1088/1748-0221/12/02/P02014,arXiv:1607.03663

  29. [29]

    Performance of missing transverse momentum reconstruction in proton-proton collisions at √s=13 TeV using the CMS detector

    CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at √s=13 TeV using the CMS detector”,JINST14(2019) P07004,doi:10.1088/1748-0221/14/07/P07004,arXiv:1903.06078

  30. [30]

    Jet trimming

    D. Krohn, J. Thaler, and L.-T. Wang, “Jet trimming”,JHEP02(2010) 084, doi:10.1007/JHEP02(2010)084,arXiv:0912.1342. References 15

  31. [31]

    Jet algorithms performance in 13 TeV data

    CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary CMS-PAS-JME-16-003, 2017

  32. [32]

    Interaction networks for the identification of boosted H→b b decays

    E. A. Moreno et al., “Interaction networks for the identification of boosted H→b b decays”,Phys. Rev. D102(2020) 012010,doi:10.1103/PhysRevD.102.012010, arXiv:1909.12285

  33. [33]

    Jet tagging via particle clouds

    H. Qu and L. Gouskos, “Jet tagging via particle clouds”,Phys. Rev. D101(2020) 056019, doi:10.1103/PhysRevD.101.056019,arXiv:1902.08570

  34. [34]

    Identification of hadronic tau lepton decays using a deep neural network

    CMS Collaboration, “Identification of hadronic tau lepton decays using a deep neural network”,JINST17(2022) P07023,doi:10.1088/1748-0221/17/07/P07023, arXiv:2201.08458

  35. [35]

    Precision luminosity measurement in proton-proton collisions at√s=13 TeV in 2015 and 2016 at CMS

    CMS Collaboration, “Precision luminosity measurement in proton-proton collisions at√s=13 TeV in 2015 and 2016 at CMS”,Eur. Phys. J. C81(2021) 800, doi:10.1140/epjc/s10052-021-09538-2,arXiv:2104.01927

  36. [36]

    CMS luminosity measurement for the 2017 data-taking period at√s= 13 TeV

    CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at√s= 13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018

  37. [37]

    CMS luminosity measurement for the 2018 data-taking period at√s= 13 TeV

    CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at√s= 13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019

  38. [38]

    A method for correcting the substructure of multiprong jets using the Lund jet plane

    CMS Collaboration, “A method for correcting the substructure of multiprong jets using the Lund jet plane”,JHEP11(2025) 038,doi:10.1007/JHEP11(2025)038, arXiv:2507.07775

  39. [39]

    Confidence level computation for combining searches with small statistics

    T. Junk, “Confidence level computation for combining searches with small statistics”, Nucl. Instrum. Meth. A434(1999) 435,doi:10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006

  40. [40]

    Presentation of search results: the CLs technique

    A. L. Read, “Presentation of search results: the CLs technique”,J. Phys. G28(2002) 2693, doi:10.1088/0954-3899/28/10/313

  41. [41]

    Asymptotic formulae for likelihood-based tests of new physics

    G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,Eur. Phys. J. C71(2011) 1554, doi:10.1140/epjc/s10052-011-1554-0,arXiv:1007.1727. [Erratum: doi:10.1140/epjc/s10052-013-2501-z]

  42. [42]

    The CMS statistical analysis and combination tool: COMBINE

    CMS Collaboration, “The CMS statistical analysis and combination tool: COMBINE”, Comput. Softw. Big Sci.8(2024) 19,doi:10.1007/s41781-024-00121-4, arXiv:2404.06614. 16 17 A The CMS Collaboration Yerevan Physics Institute, Yerevan, Armenia A. Gevorgyan , A. Hayrapetyan, V . Makarenko , A. Tumasyan1 Marietta Blau Institute for Particle Physics, Vienna, Aust...