Pith. sign in

REVIEW 2 major objections 4 minor 1 cited by

Constraints on standard model effective field theory for a Higgs boson produced in association with W or Z bosons in the H $\to\mathrm{b\bar{b}}$ decay channel in proton-proton collisions at $\sqrt{s}$ = 13 TeV

T0 review · 2 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Pith's one-line read In VH(H→bb) production at 13 TeV, six SMEFT Wilson coefficients are simultaneously constrained and all match the standard model.

desk verdict A careful and genuinely new SMEFT extraction in VH(bb) using likelihood-free inference; the quadratic-model interval labeling is the only real soft spot. read the letter →

arxiv 2411.16907 v2 pith:W5Y67FKL submitted 2024-11-25 hep-ex

classification hep-ex
keywords SMEFTHiggs-strahlungVHproductionHtobbbardecayWilsoncoefficientslikelihood-freeinferenceboostedinformationtreesCPviolation
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 asks whether LHC data on producing a Higgs boson together with a W or Z boson, with the Higgs decaying into bottom quarks, show any sign of new physics beyond the standard model. Working with 138 inverse femtobarns of 13 TeV CMS data, it fits six dimension-six SMEFT operator coefficients simultaneously and reports that all are consistent with zero. It is the first analysis in this channel to include angular observables sensitive to the CP structure of the Higgs interaction, and the most complete SMEFT interpretation of VH(H→bb) to date. If its conclusions hold, they would rule out a large class of nonresonant new-physics effects at the TeV scale in Higgs-strahlung production while tightening the global SMEFT bounds on these operators.

What carries the argument

The central object is the boosted information tree (BIT), a likelihood-free estimator of the likelihood ratio R(x|θ, θ0) between different SMEFT hypotheses, built from per-event matrix-element weights computed with SMEFTsim. The quadratic dependence on the six Wilson coefficients is decomposed into linear, quadratic, and mixed components of the likelihood ratio, and a Bayesian optimization of the binned template shape selects the working point in coefficient space that maximizes fully profiled sensitivity to all six coefficients. The angular basis from Ref. [38] supplies the CP-sensitive angular functions, and the coefficient basis is rotated to the mass-eigenstate basis, defining gZZ2 and gZZ4 and removing unconstrained directions in the Wilson coefficient space.

What would settle it

Re-extract the quadratic-model intervals using a Neyman construction or Monte Carlo calibration on simulated pseudo-data and check whether the q < 1 and q < 4 thresholds actually have 68% and 95% coverage.

Watch

Extended reading notes

Core claim

The paper claims that in the VH(H→bb) process at √s = 13 TeV with 138 fb⁻¹ of data, a simultaneous profiled maximum-likelihood fit to six dimension-six SMEFT Wilson coefficients — cHq1, cHq3, cHu, cHd, gZZ2, and gZZ4 — yields results consistent with the standard model. For the first time in this channel, angular observables sensitive to the CP structure of the H-V interaction are included, via a likelihood-free inference method (boosted information trees) that learns the likelihood ratio from simulated SMEFT weights. The compatibility p-values are 73% for the linear and 84% for the quadratic SMEFT expansion. The analysis claims to be the most comprehensive SMEFT interpretation in the VH(H→bb) channel to date, with constraints on vector-coupling operators generally tighter than those on gauge-coupling operators, and it reports profiled lower limits on the new-physics scale Λ for three assumptions about coefficient magnitudes.

Load-bearing premise

That the q < 1 and q < 4 thresholds define 68% and 95% confidence intervals for the quadratic SMEFT parametrization, a premise the paper itself flags as not guaranteed because Wilks-theorem regularity conditions are violated.

Editorial extensions

If this is right

  • The linear-model constraints, which have proper coverage, provide robust bounds on the current operators, especially cHq3, in the VH(bb) channel.
  • Quadratic SMEFT terms dominate the sensitivity for most coefficients, so future data will tighten these bounds more than linearly.
  • The BIT template-optimization procedure can be extended to other multi-operator EFT analyses where profiling several coefficients degrades sensitivity.
  • The inclusion of CP-sensitive angular observables opens a direct path to constrain CP-violating couplings in VH production.
  • These constraints can be combined with electroweak-precision and top-quark SMEFT fits to tighten global limits on the same six operators.

Reading between the lines

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

  • The paper leaves implicit that the quadratic-model intervals should not be quoted as confidence intervals; only the linear-model q < 1 and q < 4 thresholds carry a coverage guarantee.
  • Because the data are statistically limited, the hierarchy of uncertainties suggests that a future high-luminosity run with the same method could push the new-physics scale limits to several TeV for weakly coupled coefficients.
  • The template-optimization routine used here is a general solution to the profiled-EFT sensitivity problem and could be reused in global SMEFT fits outside this channel.
  • The angular decomposition exploited in the 1- and 2-lepton channels is suppressed in the 0-lepton channel by final-state topology; extending similar observables to hadronic V decays would recover CP sensitivity there.
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

2 major / 4 minor

Summary. This paper reports a CMS measurement of standard model effective field theory (SMEFT) Wilson coefficients in VH production with H to bbbar decays, using 138 fb^-1 of proton-proton collision data at sqrt(s) = 13 TeV. The analysis combines 0-, 1-, and 2-lepton channels in resolved and boosted jet topologies and uses the boosted information tree (BIT) likelihood-free inference method to construct observables sensitive to six dimension-six operator coefficients: cHq1, cHq3, cHu, cHd, gZZ2, and gZZ4. One-dimensional profiled and frozen likelihood scans are presented for linear and quadratic SMEFT parametrizations, along with two-dimensional scans and lower limits on the cutoff scale Lambda. The observed results are reported as consistent with the standard model, with compatibility p-values of 73% for the linear model and 84% for the quadratic model.

Significance. This is a technically ambitious and generally well-executed analysis. It is the first CMS study in the VH(bb) channel to use likelihood-free inference with the BIT method to probe several SMEFT operators simultaneously, including CP-sensitive angular information, and it provides a six-dimensional constraint set that goes beyond earlier STXS-based interpretations. The paper is careful in its background model, with dedicated control regions, in-situ flavor-tagging scale factors, a comprehensive systematic uncertainty model, goodness-of-fit checks, and public HEPData tables. A notable strength is the explicit disclosure in Sec. 9 that the quadratic-model likelihood-ratio intervals may under- or over-cover because Wilks regularity conditions are violated. The central SM-consistency claim is robust and independently supported by the linear-model p-value of 73%, so the coverage issue does not threaten that conclusion.

major comments (2)
  1. [Section 9, Fig. 7] The quadratic-model intervals are presented with q<1 and q<4 thresholds even though the text states that the Wilks regularity conditions are violated and that "The likelihood ratio intervals for the quadratic model may thus undercover or overcover." The Fig. 7 caption does not repeat this caveat, and the abstract and summary present constraints without qualification. The problem is not merely academic: the profiled quadratic q<1 interval for cHq1 is the union [-0.068,-0.028] U [0.0085,0.074], which excludes the SM value zero, so a reader treating q<1 as a 68% confidence interval would see a tension that is inconsistent with the quoted 84% compatibility p-value. Please calibrate these intervals (e.g., with an MC-based coverage or Neyman construction) or relabel them prominently and consistently as likelihood-ratio intervals without confidence-level interpretation, including in the figure, the abstract, and the summary.
  2. [Section 9, Fig. 8] The lower limits on the energy scale Lambda in Fig. 8 are obtained from the q=4 thresholds on the Wilson coefficients, as stated in the text: "The upper limits on the Wilson coefficients corresponding to q = 4 is used for translating the constraints to Lambda." For the quadratic parametrization these thresholds are subject to the same coverage caveat, so the Lambda limits inherit it. If the quadratic-model Lambda limits are retained as quantitative results, they should be derived from calibrated intervals or explicitly labeled as non-coverage likelihood-ratio bounds.
minor comments (4)
  1. [Section 7.1, Eq. (10)] The test statistic is written as q_theta = -log[L(D|theta)/L(D|theta0)] without the factor 2, whereas the thresholds q<1 and q<4 and the Delta(-2 ln L) axes in the figures correspond to the usual -2 log-likelihood ratio; please make the definition consistent.
  2. [Section 9] The sentence "The modified frequentist approach [117-119] is used in this search to set intervals" appears to describe a CLs procedure, but the reported intervals are asymptotic likelihood-ratio thresholds; either remove the sentence or explain how the CLs approach was used.
  3. [Section 4] The statement "A sufficient number of nominal values are simulated which allows the interpolation to recover the full polynomial EFT dependency" is too vague; please specify the number of simulation points and the coefficient values used to fix the quadratic polynomial in six coefficients.
  4. [Section 9] The sentence "For all Wilson coefficients, the quadratic components dominate the SMEFT sensitivity, except for cHq3, where the linear and quadratic terms have comparable sensitivity and therefore result in better constraints on the Wilson coefficient observed values" is unclear; comparable linear and quadratic contributions do not by themselves explain the better constraints, and the sentence should be rephrased.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the Wilson coefficients are fit outputs, and the likelihood-ratio template is trained on independent simulation with reserved evaluation events.

full rationale

The central claim is a measurement: six SMEFT Wilson coefficients are extracted from a profiled maximum-likelihood fit to 138 fb^-1 of CMS data. The SMEFT predictions enter the likelihood as simulated event weights from SMEFTsim and MadGraph, and the BIT regression targets are the matrix-element-level derivatives of the joint likelihood ratio. The trained polynomial is then evaluated on the 50% of events held out from training, as stated in Section 7.2: 'A fraction of 50% of the events are used for the training, which are then removed from the analysis.' No fitted parameter is recycled as a prediction, and no reported quantity is defined in terms of itself. The only load-bearing imported results are the SMEFT operator basis and the BIT likelihood-ratio formalism, both of which are stated explicitly in Eqs. (1)-(18) and validated internally through the background-free closure test and goodness-of-fit tests. The paper itself flags a coverage limitation for the quadratic-model intervals in Section 9: 'The likelihood ratio intervals for the quadratic model may thus undercover or overcover.' This is a statistical-coverage caveat, not a circularity: the linear-model intervals have correct Wilks coverage, and the SM-compatibility conclusion is supported by both the linear (73%) and quadratic (84%) p-values. Citations to Refs. [11,42,43] are methodological and are not used to forbid alternatives or to define the measured result. No anonymous or self-citation chain is load-bearing. Verdict: no significant circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The six Wilson coefficients are parameters of interest, not hidden inputs. The free parameters listed here are nuisance parameters fitted to data or derived from control regions. No invented entities are introduced. The axioms are the standard SMEFT and statistical assumptions, including one the paper itself flags as violated, namely asymptotic coverage for quadratic intervals.

free parameters (3)
  • Background normalization scale factors for tt, V+LF, V+HF = The parameters are allowed to float in the maximum-likelihood fit.
    Freely floating rate parameters constrained mainly by control regions; they affect the background model in the signal regions and therefore the Wilson coefficient constraints.
  • In-situ flavor-tagging scale factors in boosted categories = The parameters are unconstrained in the fit.
    Additional parameters account for data/simulation differences in high-pT jet tagging efficiencies; they are treated as fully correlated between channels.
  • V+jets shape reweighting parameters = The parameters are derived from data in control regions.
    Correction factors derived in the V+HF control region to fix Delta R(bb) mismodeling and in the 2-lepton channel for low pT(H) mismodeling; they are propagated as shape uncertainties.
assumptions (5)
  • domain assumption SMEFT truncation at dimension-six operators; dimension-eight interference with the SM is neglected.
    Standard EFT assumption used in Sec. 2; the paper states the dimension-eight interference is of the same order as the squared dimension-six terms but is neglected.
  • domain assumption Signal acceptance in the EFT-sensitive phase space is unchanged by the SMEFT operators considered.
    Checked in Sec. 6: 'It was checked that the signal acceptance in the EFT-sensitive phase space does not change in the presence of the SMEFT operators considered in this analysis.'
  • domain assumption No SMEFT effects in background processes.
    Backgrounds are modeled under the SM; any EFT contamination is neglected. Sec. 7.1 states: 'For the background processes, since no EFT effects are considered, Ri and Ri,j are always zero.'
  • domain assumption Asymptotic Wilks coverage for the quadratic-model likelihood-ratio intervals.
    Sec. 9 states the quadratic model violates Wilks theorem regularity conditions and intervals may under/overcover, yet q less than 1 and q less than 4 are used for all reported intervals.
  • domain assumption Gluon-induced ZH is insensitive to the considered operators.
    Sec. 2 invokes Ref. [47] and notes the normalization of this process does not affect sensitivity to the Wilson coefficients.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Constraints on standard model effective field theory for a Higgs boson produced in association with W or Z bosons in the H $\to\mathrm{b\bar{b}}$ decay channel in proton-proton collisions at $\sqrt{s}$ = 13 TeV." pith.science (2026). https://pith.science/paper/W5Y67FKL

@misc{pith2026241116907,
  author       = {Pith},
  title        = {Pith review of: Constraints on standard model effective field theory for a Higgs boson produced in association with W or Z bosons in the H $\to\mathrmb\barb$ decay channel in proton-proton collisions at $\sqrts$ = 13 TeV},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W5Y67FKL}},
  note         = {Machine review of arXiv:2411.16907}
}
abstract

A standard model effective field theory (SMEFT) analysis with dimension-six operators probing nonresonant new physics effects is performed in the Higgs-strahlung process, where the Higgs boson is produced in association with a W or Z boson, in proton-proton collisions at a center-of-mass energy of 13 TeV. The final states in which the W or Z boson decays leptonically and the Higgs boson decays to a pair of bottom quarks are considered. The analyzed data were collected by the CMS experiment between 2016 and 2018 and correspond to an integrated luminosity of 138 fb$^{-1}$. An approach designed to simultaneously optimize the sensitivity to Wilson coefficients of multiple SMEFT operators is employed. Likelihood scans as functions of the Wilson coefficients that carry SMEFT sensitivity in this final state are performed for different expansions in SMEFT. The results are consistent with the predictions of the standard model.

Figures

Figures reproduced from arXiv: 2411.16907 by the authors.

Figure 1
Figure 1. Representative Feynman diagrams for VH production sensitive to different [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Decay planes and angles in the V(→ ℓ1 ℓ2 )H(→ bb) production. The Θ angle is defined in the VH rest frame, while θ is defined in the V rest frame. Figure modified from Ref. [38]. The coordinate system used in the sketch of the decay plane is independent of the general CMS coordinate system that is used for the analysis. The functions f i depend on the three angles in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Selected template shapes after the optimization process described in Section 7.3 in the [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: The BIT templates obtained using a background-only fit to data in the 2-muon (left) [PITH_FULL_IMAGE:figures/full_fig_p025_4.png]
Figure 5
Figure 5. Figure 5: The BIT templates obtained using a background-only fit to data in the 1-muon (left) [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]
Figure 6
Figure 6. Figure 6: The BIT templates obtained using a background-only fit to data in the 0-lepton final [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]
Figure 7
Figure 7. Figure 7: Summary of results in terms of best fit value of the Wilson coefficients and the inter [PITH_FULL_IMAGE:figures/full_fig_p028_7.png]
Figure 8
Figure 8. Figure 8: Profiled limits on the energy scale Λ for three different assumptions for each Wilson coefficient while fixing the other Wilson coefficients to their SM values with up to the linear (upper row) and quadratic (lower row) terms in SMEFT parameterization. The upper limits…
Figure 9
Figure 9. Figure 9: Observed two-dimensional likelihood scans for different pairs of Wilson coefficients: [PITH_FULL_IMAGE:figures/full_fig_p030_9.png]
Figure 10
Figure 10. Figure 10: Observed two-dimensional likelihood scans for different pairs of Wilson coefficients: [PITH_FULL_IMAGE:figures/full_fig_p031_10.png]
Figure 11
Figure 11. Figure 11: Observed two-dimensional likelihood scans for different pairs of Wilson coefficients: [PITH_FULL_IMAGE:figures/full_fig_p032_11.png]
Figure 12
Figure 12. Figure 12: Observed two-dimensional likelihood scans for different pairs of Wilson coefficients: [PITH_FULL_IMAGE:figures/full_fig_p033_12.png]
Figure 13
Figure 13. Figure 13: Observed two-dimensional likelihood scans for different pairs of Wilson coefficients: [PITH_FULL_IMAGE:figures/full_fig_p034_13.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Measurement of off-shell Higgs boson production in the $H^*\rightarrow ZZ\rightarrow 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV $pp$ collisions with the ATLAS detector

    hep-ex 2024-12 accept novelty 6.0 of 10

    Using neural simulation-based inference, ATLAS improves evidence for off-shell Higgs production in ZZ -> 4l to 2.5 sigma observed (1.3 sigma expected) and measures Gamma_H = 4.3 +2.7 -1.9 MeV.

Reference graph

Works this paper leans on

122 extracted references · 6 canonical work pages · cited by 1 Pith paper

  1. [1]

    Broken symmetry and the mass of gauge vector mesons

    F. Englert and R. Brout, “Broken symmetry and the mass of gauge vector mesons”, Phys. Rev. Lett. 13 (1964) 321, doi:10.1103/PhysRevLett.13.321

  2. [2]

    Broken symmetries, massless particles and gauge fields

    P . W. Higgs, “Broken symmetries, massless particles and gauge fields”, Phys. Lett. 12 (1964) 132, doi:10.1016/0031-9163(64)91136-9

  3. [3]

    Broken symmetries and the masses of gauge bosons

    P . W. Higgs, “Broken symmetries and the masses of gauge bosons”, Phys. Rev. Lett. 13 (1964) 508, doi:10.1103/PhysRevLett.13.508

  4. [4]

    Global conservation laws and massless particles

    G. S. Guralnik, C. R. Hagen, and T. W. B. Kibble, “Global conservation laws and massless particles”, Phys. Rev. Lett. 13 (1964) 585, doi:10.1103/PhysRevLett.13.585. 34

  5. [5]

    Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

    ATLAS Collaboration, “Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC”, Phys. Lett. B 716 (2012) 1, doi:10.1016/j.physletb.2012.08.020, arXiv:1207.7214

  6. [6]

    Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC

    CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”, Phys. Lett. B 716 (2012) 30, doi:10.1016/j.physletb.2012.08.021, arXiv:1207.7235

  7. [7]

    Observation of a new boson with mass near 125 GeV in pp collisions at √s = 7 and 8 TeV

    CMS Collaboration, “Observation of a new boson with mass near 125 GeV in pp collisions at √s = 7 and 8 TeV”, JHEP 06 (2013) 081, doi:10.1007/JHEP06(2013)081, arXiv:1303.4571

  8. [8]

    Observation of H → bb decays and VH production with the ATLAS detector

    ATLAS Collaboration, “Observation of H → bb decays and VH production with the ATLAS detector”, Phys. Lett. B 786 (2018) 59, doi:10.1016/j.physletb.2018.09.013, arXiv:1808.08238

Show all 122 references
  1. [9]

    Observation of Higgs boson decay to bottom quarks

    CMS Collaboration, “Observation of Higgs boson decay to bottom quarks”, Phys. Rev. Lett. 121 (2018) 121801, doi:10.1103/PhysRevLett.121.121801, arXiv:1808.08242

  2. [10]

    Measurements of WH and ZH production with Higgs boson decays into bottom quarks and direct constraints on the charm Yukawa coupling in 13 TeV pp collisions with the ATLAS detector

    ATLAS Collaboration, “Measurements of WH and ZH production with Higgs boson decays into bottom quarks and direct constraints on the charm Yukawa coupling in 13 TeV pp collisions with the ATLAS detector”, 2024.arXiv:2410.19611. submitted to JHEP

  3. [11]

    CMS Collaboration, “Measurement of simplified template cross sections of the Higgs boson produced in association with W or Z bosons in the H →b¯b decay channel in proton-proton collisions at √s =13 TeV”, Phys. Rev. D 109 (2024) 092011, doi:0.1103/PhysRevD.109.092011, arXiv:2312.07562

  4. [12]

    Effective Lagrangian analysis of new interactions and flavor conservation

    W. Buchmuller and D. Wyler, “Effective Lagrangian analysis of new interactions and flavor conservation”, Nucl. Phys. B 268 (1986) 621, doi:10.1016/0550-3213(86)90262-2

  5. [13]

    Operator analysis for precision electroweak physics

    B. Grinstein and M. B. Wise, “Operator analysis for precision electroweak physics”, Phys. Lett. B 265 (1991) 326, doi:10.1016/0370-2693(91)90061-T

  6. [14]

    Electroweak corrections in high energy processes using effective field theory

    J.-y. Chiu, F. Golf, R. Kelley, and A. V . Manohar, “Electroweak corrections in high energy processes using effective field theory”, Phys. Rev. D 77 (2008) 053004, doi:10.1103/PhysRevD.77.053004, arXiv:0712.0396

  7. [15]

    Effective field theory: A modern approach to anomalous couplings

    C. Degrande et al., “Effective field theory: A modern approach to anomalous couplings”, Annals Phys. 335 (2013) 21, doi:10.1016/j.aop.2013.04.016, arXiv:1205.4231

  8. [16]

    Renormalization group evolution of the standard model dimension six operators I: formalism and lambda dependence

    E. E. Jenkins, A. V . Manohar, and M. Trott, “Renormalization group evolution of the standard model dimension six operators I: formalism and lambda dependence”, JHEP 10 (2013) 087, doi:10.1007/JHEP10(2013)087, arXiv:1308.2627

  9. [17]

    Renormalization group evolution of the standard model dimension six operators III: gauge coupling dependence and phenomenology

    R. Alonso, E. E. Jenkins, A. V . Manohar, and M. Trott, “Renormalization group evolution of the standard model dimension six operators III: gauge coupling dependence and phenomenology”, JHEP 04 (2014) 159, doi:10.1007/JHEP04(2014)159, arXiv:1312.2014. References 35

  10. [18]

    Renormalization group evolution of the standard model dimension six operators II: Yukawa dependence

    E. E. Jenkins, A. V . Manohar, and M. Trott, “Renormalization group evolution of the standard model dimension six operators II: Yukawa dependence”, JHEP 01 (2014) 035, doi:10.1007/JHEP01(2014)035, arXiv:1310.4838

  11. [19]

    Effective theories and measurements at colliders

    C. Englert and M. Spannowsky, “Effective theories and measurements at colliders”, Phys. Lett. B 740 (2015) 8, doi:10.1016/j.physletb.2014.11.035, arXiv:1408.5147

  12. [20]

    The standard model as an effective field theory

    I. Brivio and M. Trott, “The standard model as an effective field theory”, Phys. Rept. 793 (2019) 1, doi:10.1016/j.physrep.2018.11.002, arXiv:1706.08945

  13. [21]

    The standard model effective field theory at work

    G. Isidori, F. Wilsch, and D. Wyler, “The standard model effective field theory at work”, Rev. Mod. Phys. 96 (2024) 015006, doi:10.1103/RevModPhys.96.015006, arXiv:2303.16922

  14. [22]

    Constraints on anomalous Higgs boson couplings to vector bosons and fermions in its production and decay using the four-lepton final state

    CMS Collaboration, “Constraints on anomalous Higgs boson couplings to vector bosons and fermions in its production and decay using the four-lepton final state”, Phys. Rev. D 104 (2021) 052004, doi:10.1103/PhysRevD.104.052004, arXiv:2104.12152

  15. [23]

    Constraints on anomalous Higgs boson couplings from its production and decay using the WW channel in proton–proton collisions at√s = 13 TeV

    CMS Collaboration, “Constraints on anomalous Higgs boson couplings from its production and decay using the WW channel in proton–proton collisions at√s = 13 TeV”, Eur. Phys. J. C 84 (2024) 779, doi:10.1140/epjc/s10052-024-12925-0 , arXiv:2403.00657

  16. [24]

    Constraints on anomalous Higgs boson couplings to vector bosons and fermions from the production of Higgs bosons using the ττ final state

    CMS Collaboration, “Constraints on anomalous Higgs boson couplings to vector bosons and fermions from the production of Higgs bosons using the ττ final state”, Phys. Rev. D 108 (2023) 032013, doi:10.1103/PhysRevD.108.032013, arXiv:2205.05120

  17. [25]

    Spin determination of single-produced resonances at hadron colliders

    Y. Gao et al., “Spin determination of single-produced resonances at hadron colliders”, Phys. Rev. D 81 (2010) 075022, doi:10.1103/PhysRevD.81.075022, arXiv:1001.3396

  18. [26]

    On the spin and parity of a single-produced resonance at the LHC

    S. Bolognesi et al., “On the spin and parity of a single-produced resonance at the LHC”, Phys. Rev. D 86 (2012) 095031, doi:10.1103/PhysRevD.86.095031, arXiv:1208.4018

  19. [27]

    Constraining anomalous HVV interactions at proton and lepton colliders

    I. Anderson et al., “Constraining anomalous HVV interactions at proton and lepton colliders”, Phys. Rev. D 89 (2014) 035007, doi:10.1103/PhysRevD.89.035007, arXiv:1309.4819

  20. [28]

    Measurement of VH, H → bb production as a function of the vector-boson transverse momentum in 13 TeV pp collisions with the ATLAS detector

    ATLAS Collaboration, “Measurement of VH, H → bb production as a function of the vector-boson transverse momentum in 13 TeV pp collisions with the ATLAS detector”, JHEP 05 (2019) 141, doi:10.1007/JHEP05(2019)141, arXiv:1903.04618

  21. [29]

    Measurements of WH and ZH production in the H → bb decay channel in pp collisions at 13 TeV with the ATLAS detector

    ATLAS Collaboration, “Measurements of WH and ZH production in the H → bb decay channel in pp collisions at 13 TeV with the ATLAS detector”, Eur. Phys. J. C 81 (2021) 178, doi:10.1140/epjc/s10052-020-08677-2 , arXiv:2007.02873

  22. [30]

    Combined Higgs boson production and decay measurements with up to 137 fb−1 of proton-proton collision data at √s = 13 TeV

    CMS Collaboration, “Combined Higgs boson production and decay measurements with up to 137 fb−1 of proton-proton collision data at √s = 13 TeV”, CMS Physics Analysis Summary CMS-PAS-HIG-19-005, 2020

  23. [31]

    Top, higgs, diboson and electroweak fit to the standard model effective field theory

    J. Ellis et al., “Top, higgs, diboson and electroweak fit to the standard model effective field theory”, JHEP 04 (2021) 279, doi:10.1007/JHEP04(2021)279, arXiv:2012.02779. 36

  24. [32]

    Combined SMEFT interpretation of Higgs, diboson, and top quark data from the LHC

    SMEFiT Collaboration, “Combined SMEFT interpretation of Higgs, diboson, and top quark data from the LHC”, JHEP 11 (2021) 089, doi:10.1007/JHEP11(2021)089, arXiv:2105.00006

  25. [33]

    HEPData record for this analysis

    “HEPData record for this analysis”, 2024. doi:10.17182/hepdata.155497

  26. [34]

    Baryon- and lepton-nonconserving processes

    S. Weinberg, “Baryon- and lepton-nonconserving processes”, Phys. Rev. Lett. 43 (1979) 1566, doi:10.1103/PhysRevLett.43.1566

  27. [35]

    Dimension-six terms in the standard model Lagrangian

    B. Grzadkowski, M. Iskrzynski, M. Misiak, and J. Rosiek, “Dimension-six terms in the standard model Lagrangian”, JHEP 10 (2010) 085, doi:10.1007/JHEP10(2010)085, arXiv:1008.4884

  28. [36]

    Model-independent precision constraints on dimension-6 operators

    A. Falkowski and F. Riva, “Model-independent precision constraints on dimension-6 operators”, JHEP 02 (2015) 039, doi:10.1007/JHEP02(2015)039, arXiv:1411.0669

  29. [37]

    Probing electroweak precision physics via boosted Higgs-strahlung at the LHC

    S. Banerjee, C. Englert, R. S. Gupta, and M. Spannowsky, “Probing electroweak precision physics via boosted Higgs-strahlung at the LHC”, Phys. Rev. D 98 (2018) 095012, doi:10.1103/PhysRevD.98.095012, arXiv:1807.01796

  30. [38]

    Towards the ultimate differential SMEFT analysis

    S. Banerjee et al., “Towards the ultimate differential SMEFT analysis”, JHEP 09 (2020) 170, doi:10.1007/JHEP09(2020)170, arXiv:1912.07628

  31. [39]

    Constraining effective field theories with machine learning

    J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “Constraining effective field theories with machine learning”, Phys. Rev. Lett. 121 (2018) 111801, doi:10.1103/PhysRevLett.121.111801, arXiv:1805.00013

  32. [40]

    Mining gold from implicit models to improve likelihood-free inference

    J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer, “Mining gold from implicit models to improve likelihood-free inference”, Proc. Nat. Acad. Sci. 117 (2020) 5242, doi:10.1073/pnas.1915980117, arXiv:1805.12244

  33. [41]

    Parametrized classifiers for optimal EFT sensitivity

    S. Chen, A. Glioti, G. Panico, and A. Wulzer, “Parametrized classifiers for optimal EFT sensitivity”, JHEP 05 (2021) 247, doi:10.1007/JHEP05(2021)247, arXiv:2007.10356

  34. [42]

    Tree boosting for learning EFT parameters

    S. Chatterjee et al., “Tree boosting for learning EFT parameters”, Comput. Phys. Commun. 277 (2022) 108385, doi:10.1016/j.cpc.2022.108385, arXiv:2107.10859

  35. [43]

    Learning the EFT likelihood with tree boosting

    S. Chatterjee, S. Rohshap, R. Sch ¨ofbeck, and D. Schwarz, “Learning the EFT likelihood with tree boosting”, 2022. arXiv:2205.12976

  36. [44]

    Unbinned multivariate observables for global SMEFT analyses from machine learning

    R. Gomez Ambrosio et al., “Unbinned multivariate observables for global SMEFT analyses from machine learning”, JHEP 03 (2023) 033, doi:10.1007/JHEP03(2023)033, arXiv:2211.02058

  37. [45]

    Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector

    LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector”, CERN Report CERN-2017-002-M, 2016. doi:10.23731/CYRM-2017-002, arXiv:1610.07922

  38. [46]

    Constraining anomalous Higgs boson couplings to virtual photons

    J. Davis et al., “Constraining anomalous Higgs boson couplings to virtual photons”, Phys. Rev. D 105 (2022) 096027, doi:10.1103/PhysRevD.105.096027, arXiv:2109.13363. References 37

  39. [47]

    Diboson production in the SMEFT from gluon fusion

    A. Rossia, M. Thomas, and E. Vryonidou, “Diboson production in the SMEFT from gluon fusion”, JHEP 11 (2023) 132, doi:10.1007/JHEP11(2023)132, arXiv:2306.09963

  40. [48]

    The CMS experiment at the CERN LHC

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

  41. [49]

    Development of the CMS detector for the CERN LHC Run 3

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

  42. [50]

    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”, JINST 9 (2014) P10009, doi:10.1088/1748-0221/9/10/P10009, arXiv:1405.6569

  43. [51]

    The CMS phase-1 pixel detector upgrade

    CMS Tracker Group, “The CMS phase-1 pixel detector upgrade”, JINST 16 (2021) P02027, doi:10.1088/1748-0221/16/02/P02027, arXiv:2012.14304

  44. [52]

    Track impact parameter resolution for the full pseudo rapidity coverage in the 2017 dataset with the CMS phase-1 pixel detector

    CMS Collaboration, “Track impact parameter resolution for the full pseudo rapidity coverage in the 2017 dataset with the CMS phase-1 pixel detector”, CMS Detector Performance Summary CMS-DP-2020-049, 2020

  45. [53]

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

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

  46. [54]

    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”, JINST 15 (2020) P10017, doi:10.1088/1748-0221/15/10/P10017, arXiv:2006.10165

  47. [55]

    The CMS trigger system

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

  48. [56]

    Performance of the CMS muon trigger system in proton-proton collisions at 13 TeV

    CMS Collaboration, “Performance of the CMS muon trigger system in proton-proton collisions at 13 TeV”, JINST 16 (2021) P07001, doi:10.1088/1748-0221/16/07/P07001, arXiv:2102.04790

  49. [57]

    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”, JHEP 11 (2004) 040, doi:10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146

  50. [58]

    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”, JHEP 11 (2007) 070, doi:10.1088/1126-6708/2007/11/070, arXiv:0709.2092

  51. [59]

    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”, JHEP 06 (2010) 043, doi:10.1007/JHEP06(2010)043, arXiv:1002.2581

  52. [60]

    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”, JHEP 09 (2007) 126, doi:10.1088/1126-6708/2007/09/126, arXiv:0707.3088. 38

  53. [61]

    Top++: a program for the calculation of the top-pair cross-section at hadron colliders

    M. Czakon and A. Mitov, “Top++: a program for the calculation of the top-pair cross-section at hadron colliders”, Comput. Phys. Commun. 185 (2014) 2930, doi:10.1016/j.cpc.2014.06.021, arXiv:1112.5675

  54. [62]

    Single-topt-channel hadroproduction in the four-flavour scheme with POWHEG and aMC@NLO

    R. Frederix, E. Re, and P . Torrielli, “Single-topt-channel hadroproduction in the four-flavour scheme with POWHEG and aMC@NLO”, JHEP 09 (2012) 130, doi:10.1007/JHEP09(2012)130, arXiv:1207.5391

  55. [63]

    Single-top Wt-channel production matched with parton showers using the POWHEG method

    E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEG method”, Eur. Phys. J. C 71 (2011) 1547, doi:10.1140/epjc/s10052-011-1547-z , arXiv:1009.2450

  56. [64]

    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”, JHEP 07 (2014) 079, doi:10.1007/JHEP07(2014)079, arXiv:1405.0301

  57. [65]

    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”, JHEP 03 (2013) 015, doi:10.1007/JHEP03(2013)015, arXiv:1212.3460

  58. [66]

    Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions

    J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”, Eur. Phys. J. C 53 (2008) 473, doi:10.1140/epjc/s10052-007-0490-5 , arXiv:0706.2569

  59. [67]

    Merging meets matching in MC@NLO

    R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”, JHEP 12 (2012) 061, doi:10.1007/JHEP12(2012)061, arXiv:1209.6215

  60. [68]

    LHC EFT WG Note: SMEFT predictions, event reweighting, and simulation

    A. Belvedere et al., “LHC EFT WG Note: SMEFT predictions, event reweighting, and simulation”, CERN Report CERN-LHCEFTWG-2024-001, 2024. arXiv:2406.14620

  61. [69]

    MINLO: multi-scale improved NLO

    K. Hamilton, P . Nason, and G. Zanderighi, “MINLO: multi-scale improved NLO”, JHEP 10 (2012) 155, doi:10.1007/JHEP10(2012)155, arXiv:1206.3572

  62. [70]

    HW/HZ + 0 and 1 jet at NLO with the POWHEG BOX interfaced to GoSam and their merging within MiNLO

    G. Luisoni, P . Nason, C. Oleari, and F. Tramontano, “HW/HZ + 0 and 1 jet at NLO with the POWHEG BOX interfaced to GoSam and their merging within MiNLO”, JHEP 10 (2013) 083, doi:10.1007/JHEP10(2013)083, arXiv:1306.2542

  63. [71]

    The SMEFTsim package, theory and tools

    I. Brivio, Y. Jiang, and M. Trott, “The SMEFTsim package, theory and tools”, JHEP 12 (2017) 070, doi:10.1007/JHEP12(2017)070, arXiv:1709.06492

  64. [72]

    SMEFTsim 3.0 — a practical guide

    I. Brivio, “SMEFTsim 3.0 — a practical guide”, JHEP 04 (2021) 073, doi:10.1007/JHEP04(2021)073, arXiv:2012.11343

  65. [73]

    The Higgs width in the SMEFT

    I. Brivio, T. Corbett, and M. Trott, “The Higgs width in the SMEFT”, JHEP 10 (2019) 056, doi:10.1007/JHEP10(2019)056, arXiv:1906.06949

  66. [74]

    MadWeight: automatic event reweighting with matrix elements

    P . Artoisenet and O. Mattelaer, “MadWeight: automatic event reweighting with matrix elements”, in Proc. 2nd International Workshop on Prospects for Charged Higgs Discovery at Colliders (CHARGED 2008), T. Ekelof and J. Rathsman, eds., p. 025. 2008. doi:10.22323/1.073.0025

  67. [75]

    Parton distributions from high-precision collider data

    NNPDF Collaboration, “Parton distributions from high-precision collider data”, Eur. Phys. J. C 77 (2017) 663, doi:10.1140/epjc/s10052-017-5199-5 , arXiv:1706.00428. References 39

  68. [76]

    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. C 80 (2020) 4, doi:10.1140/epjc/s10052-019-7499-4 , arXiv:1903.12179

  69. [77]

    Measurement of the inelastic proton-proton cross section at√s = 13 TeV

    CMS Collaboration, “Measurement of the inelastic proton-proton cross section at√s = 13 TeV”, JHEP 07 (2018) 161, doi:10.1007/JHEP07(2018)161, arXiv:1802.02613

  70. [78]

    G EANT 4—a simulation toolkit

    GEANT4 Collaboration, “G EANT 4—a simulation toolkit”, Nucl. Instrum. Meth. A 506 (2003) 250, doi:10.1016/S0168-9002(03)01368-8

  71. [79]

    Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid

    CMS Collaboration, “Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid”, CMS Technical Proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015

  72. [80]

    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”, JINST 16 (2021) P05014, doi:10.1088/1748-0221/16/05/P05014, arXiv:2012.06888

  73. [81]

    ECAL 2016 refined calibration and Run2 summary plots

    CMS Collaboration, “ECAL 2016 refined calibration and Run2 summary plots”, CMS Detector Performance Summary CMS-DP-2020-021, 2020

  74. [82]

    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”, JINST 13 (2018) P06015, doi:10.1088/1748-0221/13/06/P06015, arXiv:1804.04528

  75. [83]

    The anti-kT jet clustering algorithm

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

  76. [84]

    FastJet user manual

    M. Cacciari, G. P . Salam, and G. Soyez, “FastJet user manual”, Eur. Phys. J. C 72 (2012) 1896, doi:10.1140/epjc/s10052-012-1896-2 , arXiv:1111.6097

  77. [85]

    Jet energy scale and resolution measurement with Run 2 legacy data collected by CMS at 13 TeV

    CMS Collaboration, “Jet energy scale and resolution measurement with Run 2 legacy data collected by CMS at 13 TeV”, CMS Detector Performance Summary CMS-DP-2021-033, 2021

  78. [86]

    Pileup subtraction using jet areas

    M. Cacciari and G. P . Salam, “Pileup subtraction using jet areas”, Phys. Lett. B 659 (2008) 119, doi:10.1016/j.physletb.2007.09.077, arXiv:0707.1378

  79. [87]

    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”, JINST 12 (2017) P02014, doi:10.1088/1748-0221/12/02/P02014, arXiv:1607.03663

  80. [88]

    Pileup per particle identification

    D. Bertolini, P . Harris, M. Low, and N. Tran, “Pileup per particle identification”, JHEP 10 (2014) 059, doi:10.1007/JHEP10(2014)059, arXiv:1407.6013

  81. [89]

    Pileup mitigation at CMS in 13 TeV data

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

  82. [90]

    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

  83. [91]

    Jet flavour classification using DeepJet

    E. Bols et al., “Jet flavour classification using DeepJet”, JINST 15 (2020) P12012, doi:10.1088/1748-0221/15/12/P12012, arXiv:2008.10519. 40

  84. [92]

    Performance summary of AK4 jet b tagging with data from proton-proton collisions at 13 TeV

    CMS Collaboration, “Performance summary of AK4 jet b tagging with data from proton-proton collisions at 13 TeV”, CMS Detector Performance Report CMS-DP-2023-005, 2023

  85. [93]

    A deep neural network for simultaneous estimation of b jet energy and resolution

    CMS Collaboration, “A deep neural network for simultaneous estimation of b jet energy and resolution”, Comput. Softw. Big Sci. 4 (2020) 10, doi:10.1007/s41781-020-00041-z , arXiv:1912.06046

  86. [94]

    Jet tagging via particle clouds

    H. Qu and L. Gouskos, “Jet tagging via particle clouds”, Phys. Rev. D 101 (2020) 056019, doi:10.1103/PhysRevD.101.056019, arXiv:1902.08570

  87. [95]

    Jet substructure as a new Higgs search channel at the LHC

    J. M. Butterworth, A. R. Davison, M. Rubin, and G. P . Salam, “Jet substructure as a new Higgs search channel at the LHC”, Phys. Rev. Lett. 100 (2008) 242001, doi:10.1103/PhysRevLett.100.242001, arXiv:0802.2470

  88. [96]

    Towards an understanding of jet substructure

    M. Dasgupta, A. Fregoso, S. Marzani, and G. P . Salam, “Towards an understanding of jet substructure”, JHEP 09 (2013) 029, doi:10.1007/JHEP09(2013)029, arXiv:1307.0007

  89. [97]

    Soft Drop

    A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, “Soft Drop”, JHEP 05 (2014) 146, doi:10.1007/JHEP05(2014)146, arXiv:1402.2657

  90. [98]

    Better jet clustering algorithms

    Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms”, JHEP 08 (1997) 001, doi:10.1088/1126-6708/1997/08/001, arXiv:hep-ph/9707323

  91. [99]

    Hadronization corrections to jet cross-sections in deep inelastic scattering

    M. Wobisch and T. Wengler, “Hadronization corrections to jet cross-sections in deep inelastic scattering”, in Workshop on Monte Carlo Generators for HERA Physics, Hamburg, Germany, p. 270. 1998. arXiv:hep-ph/9907280

  92. [100]

    Identification of highly Lorentz-boosted heavy particles using graph neural networks and new mass decorrelation techniques

    CMS Collaboration, “Identification of highly Lorentz-boosted heavy particles using graph neural networks and new mass decorrelation techniques”, CMS Detector Performance Report CMS-DP-2020-002, 2020

  93. [101]

    Calibration of the mass-decorrelated ParticleNet tagger for boosted b¯b and c¯c jets using LHC Run 2 data

    CMS Collaboration, “Calibration of the mass-decorrelated ParticleNet tagger for boosted b¯b and c¯c jets using LHC Run 2 data”, CMS Detector Performance Report CMS-DP-2022-005, 2022

  94. [102]

    Performance of heavy-flavour jet identification in boosted topologies in proton-proton collisions at √s = 13 TeV

    CMS Collaboration, “Performance of heavy-flavour jet identification in boosted topologies in proton-proton collisions at √s = 13 TeV”, CMS Physics Analysis Summary CMS-PAS-BTV-22-001, 2023

  95. [103]

    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”, JINST 14 (2019) P07004, doi:10.1088/1748-0221/14/07/P07004, arXiv:1903.06078

  96. [104]

    Performance of Track-Corrected Missing Transverse Energy in CMS

    CMS Collaboration, “Performance of Track-Corrected Missing Transverse Energy in CMS”, CMS Physics Analysis Summary CMS-PAS-JME-09-010, 2009

  97. [105]

    Review of particle physics

    Particle Data Group, R. L. Workman et al., “Review of particle physics”, Prog. Theor. Exp. Phys. 2022 (2022) 083C01, doi:10.1093/ptep/ptac097

  98. [106]

    A guide to constraining effective field theories with machine learning

    J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “A guide to constraining effective field theories with machine learning”, Phys. Rev. D 98 (2018) 052004, doi:10.1103/PhysRevD.98.052004, arXiv:1805.00020. References 41

  99. [107]

    LightGBM: A highly efficient gradient boosting decision tree

    G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree”, in Advances in Neural Information Processing Systems 30 (NIPS 2017) , I. Guyon et al., eds. Curran Associates, Inc., 2017

  100. [108]

    A tutorial on Bayesian optimization

    P . I. Frazier, “A tutorial on Bayesian optimization”, 2018.arXiv:1807.02811

  101. [109]

    PDF4LHC recommendations for LHC run II

    J. Butterworth et al., “PDF4LHC recommendations for LHC run II”, J. Phys. G 43 (2016) 040, doi:10.1088/0954-3899/43/2/023001, arXiv:1510.03865

  102. [110]

    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. C 81 (2021) 800, doi:10.1140/epjc/s10052-021-09538-2 , arXiv:2104.01927

  103. [111]

    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

  104. [112]

    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

  105. [113]

    Fitting using finite Monte Carlo samples

    R. Barlow and C. Beeston, “Fitting using finite Monte Carlo samples”, Comput. Phys. Commun. 77 (1993) 219, doi:10.1016/0010-4655(93)90005-W

  106. [114]

    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

  107. [115]

    The RooFit toolkit for data modeling

    W. Verkerke and D. P . Kirkby, “The RooFit toolkit for data modeling”, inProc. Int. Conf. on Computing in High Energy and Nuclear Physics (CHEP03) , L. Lyons and M. Karagoz, eds., p. MOLT007. 2003. arXiv:physics/0306116

  108. [116]

    The RooStats project

    L. Moneta et al., “The RooStats project”, in Proc. 13th Int. Workshop on Advanced Computing and Analysis T echniques in Physics Research, T. Speer et al., eds., volume ACAT2010, p. 057. 2010. arXiv:1009.1003. doi:10.22323/1.093.0057

  109. [117]

    Procedure for the LHC Higgs boson search combination in Summer 2011

    ATLAS and CMS Collaborations, and LHC Higgs Combination Group, “Procedure for the LHC Higgs boson search combination in Summer 2011”, CMS Note CMS-NOTE-2011-005, ATL-PHYS-PUB-2011-11, 2011

  110. [118]

    Confidence level computation for combining searches with small statistics

    T. Junk, “Confidence level computation for combining searches with small statistics”, Nucl. Instrum. Meth. A 434 (1999) 435, doi:10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006

  111. [119]

    Presentation of search results: The CL s technique

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

  112. [120]

    Lectures on statistics in theory: Prelude to statistics in practice

    R. D. Cousins, “Lectures on statistics in theory: Prelude to statistics in practice”, 2018. arXiv:1807.05996

  113. [121]

    Cover your bases: asymptotic distributions of the profile likelihood ratio when constraining effective field theories in high-energy physics

    F. U. Bernlochner, D. C. Fry, S. B. Menary, and E. Persson, “Cover your bases: asymptotic distributions of the profile likelihood ratio when constraining effective field theories in high-energy physics”, SciPost Phys. Core 6 (2023) 013, doi:10.21468/SciPostPhysCore.6.1.013, ar...

  114. [122]

    The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses

    S. S. Wilks, “The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses”, The Ann. Math. Stat. 9 (1938) 60, doi:10.1214/aoms/1177732360. 42 43 A The CMS Collaboration Yerevan Physics Institute, Yerevan, Armenia V . Chekhovsky, A. Hayrapetyan, V . M...

Pith tools

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