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Search for Higgs boson decays into a $Z$ boson and a light hadronically decaying resonance in $pp$ collisions at $\sqrt{s}$=13 TeV with the ATLAS detector

T0 review · 0 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper searches for Higgs boson decays into a Z boson and a light resonance using the full ATLAS Run 2 dataset and finds no significant signal, setting new branching-fraction limits.

desk verdict Solid, honest ATLAS search with improved limits and a novel NN background-reweighting; the main caveat is that the extrapolation into the blinded signal region is only indirectly validated. read the letter →

arxiv 2411.16361 v2 pith:DTLRUTVX submitted 2024-11-25 hep-ex

ATLAS Collaboration This is my paper · ORCID
classification hep-ex
keywords HiggsbosonZlightresonancebranchingfractionaxion-likeparticleneuralnetworkreweightingLHCATLAS
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 tries to establish whether the Higgs boson can decay into a Z boson and a light, short-lived resonance of mass 0.5 to 3.5 GeV, such as a charmonium state, an axion-like particle, or a light pseudoscalar from a two-Higgs-doublet model. Analyzing the full 140 fb$^{-1}$ of 13 TeV proton-proton collisions recorded by ATLAS, it finds no significant excess over the Standard Model background. It therefore sets upper limits at 95% confidence: the branching fraction $B(H\to Za)$ is below about 10% for the lightest resonance masses when the light particle decays to gluons, and the effective axion coupling to the Higgs and Z bosons is constrained to 0.9 to 2 TeV$^{-1}$. The result matters because a measurable $H\to Za$ rate would be a clear sign of new particles coupling to the Higgs boson.

What carries the argument

The analysis is carried by three neural networks plus a profile-likelihood fit. A reweighting neural network, trained on the $m_{\ell\ell j}$ sidebands of 100 to 120 GeV and 140 to 180 GeV, estimates a log-likelihood ratio between Monte Carlo background and data and reweights the simulation so that its kinematic and jet-substructure distributions match the data, while the 120 to 140 GeV signal region is excluded from training. A regression neural network maps seven track-based jet-substructure variables into an estimate of the light-resonance mass, and a classification neural network then separates signal from background using those variables plus the mass estimate. Finally, a binned profile-likelihood fit to the $m_{\ell\ell j}$ distribution in the range 100 to 178 GeV, with systematic uncertainties treated as nuisance parameters, extracts the signal strength and sets limits using the $CL_s$ asymptotic formulae.

What would settle it

A future LHC dataset with more integrated luminosity should show whether the mild 1.5$\sigma$ excess near 135 GeV grows into a 3$\sigma$ or larger deviation, which would overturn the paper's no-excess conclusion, or fades away, which would confirm it.

Watch

Extended reading notes

Core claim

The paper's central claim is that, in the dataset examined, Higgs boson decays into a Z boson and a light hadronically decaying resonance are not present at a statistically significant level. For each assumed resonance mass between 0.5 and 3.5 GeV and for each decay mode ($a\to gg$ or $a\to q\bar{q}$), the observed invariant-mass distribution of the dilepton-plus-jet system is compatible with the background-only prediction. The most notable feature is a mild excess near 135 GeV in the reconstructed mass that reaches a local significance of about 1.5$\sigma$ for the $m_a=0.5$ GeV gluon-decay hypothesis, which is not enough to claim a discovery. The paper converts this null result into 95% CL upper limits on $B(H\to Za)$ and on the effective ALP coupling $C^\text{eff}_{ZH}/\Lambda$, reporting limits roughly two to three times stronger than the earlier version of this search.

Load-bearing premise

The whole result rests on the assumption that the background-reweighting neural network, trained on data from mass sidebands on either side of the signal region, predicts the background inside the 120 to 140 GeV signal region correctly, including for jet-substructure variables after the classification selection; if that extrapolation is biased, the background shape and all quoted limits would shift.

Editorial extensions

If this is right

  • If the limits are correct, any new particle that makes $H\to Za$ occur with a branching fraction above about 10% for $m_a\approx 0.5$ GeV (gluon decays) is ruled out, with the excluded range extending to higher masses at weaker limits.
  • The effective ALP coupling $C^\text{eff}_{ZH}/\Lambda$ is excluded above 0.9 to 2 TeV$^{-1}$ at 95% CL, narrowing the parameter space for a Higgs-coupled axion-like particle.
  • For charmonium states, the search cannot exclude physical branching fractions: the limits are $B(H\to Z\eta_c)>1.2$ and $B(H\to ZJ/\psi)>1.4$.
  • The factor-of-two-to-three improvement over the previous ATLAS search indicates that data-driven neural-network background reweighting substantially reduces the dominant background-modeling uncertainty.

Reading between the lines

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

  • The same sideband-reweighting technique could be ported to other searches for narrow resonances where Monte Carlo does not model jet substructure accurately, provided a signal-free sideband exists.
  • Because the sensitivity drops for higher resonance masses mostly because their decays look more like ordinary QCD jets, improvements in low-energy jet substructure or in reconstructing softer tracks could push the reach below the current 10% branching-fraction barrier.
  • The ALP limit only covers the charged three-pion decay mode; an analysis with sensitivity to the neutral $3\pi^0$ mode would be needed to close the gap for ALPs in the 0.5 to 1 GeV range.
  • The mild 1.5$\sigma$ excess near 135 GeV is a candidate fluctuation to watch in future data: if it grows with more statistics, it would most naturally appear for the lightest resonances with gluon decays and would shift the limits downward.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 5 minor

Summary. This paper presents a search for the Higgs boson decay H→Za, where a is a light hadronically decaying resonance with mass 0.5–3.5 GeV, using the full Run 2 dataset of 140 fb⁻¹ of proton–proton collisions at √s = 13 TeV recorded by ATLAS. The analysis selects events with a leptonically decaying Z boson and a highly boosted single jet containing the resonance decay products. The background is modelled by reweighting Monte Carlo simulation with a neural network trained on sidebands of the m_ℓℓj distribution, and two additional neural networks (regression and classification) are used to suppress background. A binned profile-likelihood fit to the m_ℓℓj distribution finds no significant excess over the background prediction, and 95% CL upper limits are set on B(H→Za) for both gluon and quark decay modes, as well as on the effective ALP coupling C^eff_ZH/Λ. The limits improve on the previous ATLAS result by up to a factor of two to three.

Significance. The result represents a substantial improvement over the earlier ATLAS search for H→Za in the hadronic final state, both in the data-driven background modelling and in the resulting branching-fraction limits. The analysis includes a careful treatment of systematic uncertainties, including dedicated uncertainties for the reweighting performance (control-region and bootstrap methods), alternative Monte Carlo generators, and experimental and theoretical signal uncertainties. The interpretation in terms of an ALP coupling provides new constraints on a well-motivated class of beyond-the-Standard-Model scenarios. The central claim of no excess is supported by the observed data being compatible with the background-only hypothesis across the mass range, with the largest local significance at about 1.5σ.

minor comments (5)
  1. [Sec. 5] The statement that the reweighting NN is able to reweight events in the excluded region effectively is supported only by a closure test on the adjacent bands [115,120] and [140,145] GeV, before the classification-NN requirement; the text should explicitly note that the control-region and bootstrap uncertainties described in Sec. 7 are intended to cover any residual extrapolation bias in the signal region, including the high-score tail.
  2. [Throughout] The notation for the final-state invariant mass is inconsistent (m_ℓℓj, mllj, and mℓℓj appear in the text and figures); please unify to a single symbol.
  3. [Abstract and Sec. 8] The phrase 'exclusion limit is ~10% for the lower masses' is vague; specify the exact mass range (e.g., 0.5–1 GeV) for which this holds.
  4. [Fig. 3(b)] The lower panel label 'Significance' could be confused with the significance of the fitted signal; consider renaming it to 'Per-bin residual significance' for clarity.
  5. [Sec. 4] There is a typo: 'non hard collision jets' should read 'non-hard-collision jets'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the background reweighting is trained on sidebands, the signal shapes come from MC, and the previous ATLAS result is used only for comparison.

full rationale

The paper's central claim is an observed-data limit on B(H→Za) obtained from a binned profile-likelihood fit to m_llj. The signal model is derived entirely from MC simulation (Gaussian fits to simulated m_llj distributions), with no parameter fitted to data. The background model is built by training a reweighting NN on m_llj sidebands (100–120 and 140–180 GeV) and applying the resulting weights to MC events; the signal region 120–140 GeV is excluded from training, so the background prediction in the SR is an extrapolation, not a fit to the SR. The closure test uses a separate NN with a wider excluded region and compares only adjacent bands, but this is a validation limitation, not a circular step: the SR background is not constructed from the SR data. No fitted quantity is renamed as a prediction, and no load-bearing argument reduces to a self-citation. The prior ATLAS paper [30] is cited only to compare limits, and the theoretical inputs (branching fractions, effective couplings) come from independent literature. The concern about extrapolation into the blinded SR is a systematic/correctness risk, not circularity. Thus the derivation chain is self-contained against external MC and data, and the circularity score is 0.

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

The analysis rests on standard LHC tools (MC event generators, Geant4 detector simulation, CLs statistics) and on the modeling assumption that the sideband-trained NN generalizes to the signal region. It introduces no ad hoc parameters or new physical entities; the signal strength is the measured parameter of interest, not an input assumption.

assumptions (5)
  • standard math Classifier-based density ratio estimation: p1/p2 is approximately C/(1-C) for a binary classifier C trained to separate two samples.
    Invoked in Section 5 to justify using the NN output as a MC-to-data reweighting factor, following Ref [70].
  • domain assumption The reweighting NN trained in sidebands extrapolates correctly to the blinded signal region.
    Section 5 excludes 120 < m_llj < 140 GeV from training and applies the learned weights there; validation is only in adjacent bands.
  • domain assumption Signal normalization uses the ggF Higgs production cross-section of 55.6 pb and assumes other production modes have similar acceptance.
    Section 3 states the signal samples are normalized to this cross-section; if invalid, the absolute branching-fraction limits would shift.
  • domain assumption Pythia8 MSSM scenario models the light-resonance decay fractions and hadronization, with Herwig used to estimate the uncertainty.
    Section 3 and Section 7; the dominant signal systematic comes from the Pythia/Herwig comparison and is extrapolated to masses below 2 GeV.
  • standard math Asymptotic CLs formulae provide valid 95% confidence limits.
    Section 8 uses the profile-likelihood test statistic and CLs technique with asymptotic formulae, standard for LHC searches.

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Cite this review

Pith. "Pith review of Search for Higgs boson decays into a $Z$ boson and a light hadronically decaying resonance in $pp$ collisions at $\sqrt{s}$=13 TeV with the ATLAS detector." pith.science (2026). https://pith.science/paper/DTLRUTVX

@misc{pith2026241116361,
  author       = {Pith},
  title        = {Pith review of: Search for Higgs boson decays into a $Z$ boson and a light hadronically decaying resonance in $pp$ collisions at $\sqrts$=13 TeV with the ATLAS detector},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DTLRUTVX}},
  note         = {Machine review of arXiv:2411.16361}
}
abstract

A search for decays of the Higgs boson into a $Z$ boson and a light resonance, with a mass of 0.5-3.5 GeV, is performed using the full 140 fb$^{-1}$ dataset of 13 TeV proton-proton collisions recorded by the ATLAS detector during Run 2 of the LHC. Leptonic decays of the $Z$ boson and hadronic decays of the light resonance are considered. The resonance can be interpreted as a $J/\psi$ or $\eta_c$ meson, an axion-like particle, or a light pseudoscalar in two-Higgs-doublet models. Due to its low mass, it would be produced with high boost and reconstructed as a single small-radius jet of hadrons. A neural network is used to correct the Monte Carlo simulation of the background in a data-driven way. Two additional neural networks are used to distinguish signal from background. A binned profile-likelihood fit is performed on the final-state invariant mass distribution. No significant excess of events relative to the expected background is observed, and upper limits at 95% confidence level are set on the Higgs boson's branching fraction to a $Z$ boson and a light resonance. The exclusion limit is ~10% for the lower masses, and increases for higher masses. Upper limits on the effective coupling $C^\text{eff}_{ZH}/\Lambda$ of an axion-like particle to a Higgs boson and $Z$ boson are also set at 95% confidence level, and range from 0.9 to 2 TeV$^{-1}$.

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Forward citations

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