Pith. sign in

REVIEW 4 major objections 5 minor 85 references

Advancing Higgsino Searches by Integrating ML for Boosted Object Tagging and Event Selection

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read GNN jet scores could extend higgsino search reach to 1470 GeV

desk verdict A useful methodological idea with plausible but over-optimistic projected limits; the missing GNN validation and threshold selection need scrutiny before the reach numbers are used. read the letter →

arxiv 2501.07491 v1 pith:QXEOEZNA submitted 2025-01-13 hep-ph

Rameswar Sahu , Debabrata Sahoo , Kirtiman Ghosh This is my paper · ORCID
classification hep-ph
keywords higgsinogeneralgaugemediationsupersymmetrygraphneuralnetworkboostedobjecttaggingfatjetmulticlassclassifierLHCsearch
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

The paper proposes replacing hard fat-jet tagging with continuous graph-neural-network classifier scores in a search for heavy higgsinos within general gauge mediation, and claims this materially extends the reach of hadronic LHC searches. In its simplified GGM benchmark, a boosted decision tree fed the GNN scores and kinematic variables would exclude higgsino masses up to 1470 GeV, 1390 GeV, and 1340 GeV at 95% confidence with 200 $fb^{-1}$ of 14 TeV data, for neutralino branching ratios of 100%, 50%, and 25% into h+gravitino. Those numbers exceed the ~1025 GeV benchmark of the leading existing hadronic search. The central move is to preserve every signal event by treating the tagger outputs as soft variables, rather than discarding events that fail a fixed tag threshold.

What carries the argument

The machinery is a Lorentz-equivariant graph neural network, LorentzNet, retrained as a four-class classifier (top, W/Z, Higgs, QCD) for fat jets, whose raw per-jet scores are passed directly to a per-signal-region XGBoost boosted decision tree. The BDT combines those scores with event-level kinematics—jet pT and pseudorapidity, missing energy and its significance, HT and effective mass, b-jet multiplicities, and angular separations—to separate signal from background. The final discriminant is the BDT output, cut at 0.9 and binned into five 0.02-wide bins for a binned likelihood fit. This design keeps all signal events in the analysis and lets the tree exploit the graded information in the GNN scores.

What would settle it

A rerun of the same search with an explicit multijet background estimate and with tagger systematics attached to the GNN scores, or a public calibration study of the tagger on higgsino-like fat jets, could overturn the 1470 GeV claim if the expected exclusion drops below roughly the 1025 GeV benchmark.

Watch

Extended reading notes

Core claim

The paper's central claim is that a multiclass GNN tagger's raw class scores for boosted fat jets—top, W/Z, Higgs, and QCD—are far more useful as high-level discriminating variables than as hard tags. Feeding these scores, together with event-level kinematic variables, into a per-signal-region XGBoost BDT and then doing a binned likelihood with a 0.9 score threshold yields the stated exclusion reaches. Unlike conventional NN taggers that impose an efficiency-associated threshold and drop signal, the GNN scores carry the parent-particle information through to the BDT for all events. The paper demonstrates this in the fully hadronic and semi-leptonic higgsino decay topologies of GGM SUSY, and reports the resulting 95% CL expected limits across seven mutually exclusive signal regions.

Load-bearing premise

The analysis assumes both that the GNN scores remain well-calibrated on higgsino signal jets after fast detector simulation and that the multijet background is truly negligible after preselection; if either fails, the projected reach shrinks.

Editorial extensions

If this is right

  • The projected reach of 1470 GeV at 100% Higgs-branching surpasses the ~1025 GeV benchmark of the most sensitive existing hadronic search by roughly 400 GeV.
  • The main source of improvement is using GNN scores as continuous variables; hard tagging would discard signal events at the threshold and reduce yield.
  • Reach falls to 1390 GeV and 1340 GeV as the Higgs-branching drops to 50% and 25%, consistent with losing b-tagged signal regions and growing W/Z fat-jet background overlap.
  • Because the seven signal regions are mutually exclusive, their binned BDT outputs can be statistically combined in a single limit-setting procedure.

Reading between the lines

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

  • The paper leaves tagger-specific systematic uncertainties unquantified; if GNN scores are miscalibrated on higgsino-like fat jets, the quoted reach could shrink, and a public calibration study on signal-like jets would settle that.
  • If the multijet background, which is declared negligible and not simulated, is actually present in the zero-lepton regions at 200 fb^-1, the limits would weaken; a dedicated multijet estimate is the most direct robustness check.
  • The same score-as-variable design could be transported to other GGM topologies with W/Z/h final states, such as bino-higgsino admixtures and wino or chargino NLSPs, which the paper names as future work.
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

4 major / 5 minor

Summary. The paper proposes a new search strategy for GGM higgsinos at the 14 TeV LHC with 200 fb^-1, replacing conventional fat-jet tagging with continuous scores from a GNN-based multiclass classifier (top, W/Z, Higgs, QCD) fed into a BDT. Seven mutually exclusive signal regions are defined, event samples are generated with MadGraph/Pythia/Delphes, and expected 95% CL exclusion limits are computed with pyhf. The central claim, stated in Section 5, is that this strategy can exclude higgsino masses up to 1470, 1390, and 1340 GeV for BR(higgsino -> h + gravitino) = 100%, 50%, and 25%, respectively, improving on the CMS benchmark of about 1025 GeV quoted from Ref. [52].

Significance. If the projected reach is reliable, the paper would demonstrate a useful proof-of-concept that continuous ML tagger scores, rather than hard fat-jet tags, can materially extend the hadronic reach of GGM higgsino searches. The analysis uses publicly available tools, NLO+NLL signal cross sections, and a statistical combination with pyhf, which are strengths. However, the central claim rests on unvalidated inputs: the GNN tagger performance is not shown and is deferred to a same-author reference without an arXiv identifier; the multijet background is omitted by assumption; and the systematic uncertainty is a flat 20% placeholder. These issues are load-bearing because they directly affect the projected exclusion masses, so the current evidence supports a promising proof of concept but not yet a robust sensitivity claim.

major comments (4)
  1. [Appendix A; Section 3.1] The GNN multiclass classifier is the main new ingredient, but no performance or calibration is shown. Appendix A describes a LorentzNet-based architecture and states that an extensive hyperparameter scan was performed, yet it provides no ROC curves, confusion matrices, accuracy numbers, or calibration plots. All performance validation is deferred to Ref. [89], which is a same-author paper without an arXiv identifier or public availability. This is load-bearing: the GNN scores S_h, S_t, S_v are used as BDT inputs, and the claimed improvement over the CMS benchmark requires these scores to be reliable discriminants for signal fat jets after Delphes reconstruction. The authors should either include tagger validation in the paper or provide an accessible reference, and they should quantify the impact of tagger miscalibration or domain shift on the projected limits.
  2. [Section 3.2] The multijet background is excluded by fiat. The text states that previous ATLAS searches established robust criteria that 'effectively eliminate' the multijet background, and therefore it is not simulated. This is an assumption, not a demonstration. The signal regions 0l1f0b, 0l1f1b, 0l1f2b, 0l1f3b, and 0l2f are fully hadronic with a large missing-transverse-momentum requirement, but the BDT then selects events with scores above 0.9, which may populate a different phase space than the ATLAS preselection. If multijet events pass the preselection and enter the BDT tail, the expected background is underestimated and the exclusion limits are too strong. The authors should simulate a multijet sample, use a data-driven estimate, or at least demonstrate explicitly that the BDT score distributions of multijet events are negligible after the preselection.
  3. [Section 4] The systematic uncertainty treatment is not adequate for a sensitivity claim. The paper adopts 'an overall 20% total signal/background uncertainty' following typical LHC searches, but no justification is given for applying this flat value to the specific GNN-score-based analysis. The GNN tagger transfer, the BDT threshold, and the background normalization are all sources of uncertainty that should be treated separately. A flat 20% uncertainty on every bin is not equivalent to a real systematic estimate, and the resulting limit bands in Figure 3 do not reflect the dominant theoretical uncertainties of the analysis. The authors should provide per-source systematic variations or at least show how the exclusion mass changes under reasonable variations of the GNN score calibration and background normalization.
  4. [Section 4] The BDT score threshold is chosen after inspecting the test distributions. The text says 'After carefully analyzing the signal and background BDT score distribution, we found that a cut on the BDT score at 0.9 works well.' Since the same test events are used to select the threshold and to compute the limits, this introduces an optimism bias that is not covered by the reported uncertainties. The threshold should be chosen on a validation sample separated from the events used for limit setting, or the analysis should use a scan with a trials factor or a nested procedure that accounts for the threshold choice.
minor comments (5)
  1. [Title and Abstract] The title contains a typo: 'T agging' should be 'Tagging'. The abstract is generally clear, but the phrase 'offering a significant improvement in sensitivity' is a claim that depends on the unvalidated GNN performance and should be softened until the validation is provided.
  2. [Section 1 and throughout] There are several typographical issues: 'Plank scale' should be 'Planck scale', 'Kinamatic Variables' in Appendix B should be 'Kinematic Variables', and '200 inverse femtobarn' should be '200 fb^-1' in Section 4. These should be corrected in a revision.
  3. [Figure 2] The legend labels in Figure 2 are difficult to read, with compressed names such as 'v v t t v (vv)' and 'w j j'. The authors should clarify the legend or use a table to identify the background processes, and they should define what the parentheses denote.
  4. [References] Reference [7] is missing its title and journal information, and Reference [82] has an incomplete author list and no publication details. Reference [89], which is central to the GNN validation, has no arXiv identifier and cannot be checked by readers; this should be resolved before publication.
  5. [Appendix B] The notation in Appendix B is dense and some variables are not defined precisely, for example the use of 'i' and 'j' in Delta-phi(i,j) and the exact definition of M(ji,jj). The authors should state explicitly which particles are included in each sum and whether the fat jets are excluded from HT and M_eff as indicated in Section 3.1.

Circularity Check

1 steps flagged · score 2.0 of 10

No definitional circularity in the core GNN-to-BDT chain; the mild burden is that GNN validation is deferred to an inaccessible same-author reference, and the 0.9 BDT threshold is chosen on the same test distributions used for the limits.

  1. other [Appendix A, final paragraph (page 16); Reference [89]]
    "For a detailed discussion on the performance of the classifier, we urge the interested readers to consult Ref. [89]."

    The four GNN scores are the principal new inputs to the BDT, and the projected gain over the CMS 1025 GeV benchmark depends on their being reliable discriminants. The paper provides no ROC, confusion matrix, or calibration for the tagger and defers all performance discussion to Ref. [89], an unpublished paper by the same three authors. This is a self-citation used to fill the central validation gap, though it is not the only evidence, since the BDT score distributions and limits are presented in the paper.

full rationale

The core derivation is not circular by construction: the LorentzNet-based GNN is trained on independent Pythia8 samples (ttbar, WW/ZZ, HH, dijet) to classify fat jets by parent particle, and the XGBoost BDT is trained and evaluated on held-out signal and background events after preselection, with the final limit computed from test events. The central discrimination therefore has independent content and is not equivalent to its inputs. The main fragilities are validation gaps rather than definitional circularities: (1) the GNN tagger's accuracy/calibration is deferred to Ref. [89], a same-author unpublished reference, so the score reliability is assumed rather than demonstrated; and (2) the 0.9 BDT threshold is chosen after inspecting the same test BDT score distributions that are then binned to derive the expected 95% CL limits, which can bias the projected reach upward (Section 4, page 11). The omission of the multijet background (Section 3.2) is a physics assumption that could affect the zero-lepton signal regions, but it is not a circularity. Overall, the claimed improvement over the CMS benchmark is plausible but rests on these unvalidated choices; because the central ML pipeline is trained and tested on independent samples, the circularity score is low.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The analysis contributes no new physics model; it imports a simplified GGM benchmark from the literature and adds a machine-learning analysis chain. The main implicit inputs are the assumption that the GNN tagger transfers to signal jets without calibration, exclusion of the multijet background by fiat, and a flat 20% systematic uncertainty used in the limit calculation. No genuinely new particles or mediators are invented.

free parameters (4)
  • BDT score threshold = 0.9
    Chosen after inspecting signal/background BDT score distributions in Section 4; the 95% CL limit is evaluated in bins above this cut, so the quoted reach depends directly on this hand-picked value.
  • Overall signal/background systematic uncertainty = 20%
    Assumed flat for all signal and background yields in the pyhf limit ('we assume an overall 20% total signal/background uncertainty', Section 4); no per-process or per-bin systematics are derived.
  • GNN classifier configuration = 4 Lorentz group equivariant blocks; charge node embedding; pT 300-1500 GeV in 50 GeV bins
    Selected by a hyperparameter scan in Appendix A; the tagger scores that drive the BDT depend on these choices, and no performance curves are shown.
  • Fat-jet preselection and signal-region cuts = pT > 300 GeV, invariant mass > 70 GeV; Meff > 800-1000 GeV; additional cuts in Table 1
    Hand-defined, ATLAS-inspired cuts in Table 1; not optimized with a validation procedure, and the multijet background is assumed negligible after these cuts.
assumptions (5)
  • domain assumption Simplified GGM spectrum: nearly massless gravitino LSP; chi01 decays promptly to h/Z + G; chi02 and chi±1 decay to chi01 plus soft particles; all other sparticles decouple at 5 TeV.
    Section 2 defines the signal topology and is imported from GGM literature, not derived in this paper.
  • ad hoc to paper Multijet background is negligible after preselection and can be omitted.
    Section 3.2 states the multijet background is not simulated; previous ATLAS studies are cited as justification, but no closure test is provided.
  • domain assumption Delphes with the default ATLAS card plus a flat 20% uncertainty adequately represents detector response and all systematics.
    Sections 3.1 and 4; the limit calculation relies on this ansatz, which the paper acknowledges is beyond a reliable uncertainty estimation.
  • ad hoc to paper GNN tagger trained on SM samples transfers to signal jets with no calibration shift.
    Appendix A; validation is delegated to Ref [89], which is not publicly identified, and no signal-side score calibration is shown.
  • domain assumption Background cross sections after NLO K-factors are accurate.
    Section 3.1; standard for LO event generation but an unverified input to the limit calculation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Advancing Higgsino Searches by Integrating ML for Boosted Object Tagging and Event Selection." pith.science (2026). https://pith.science/paper/QXEOEZNA

@misc{pith2026250107491,
  author       = {Pith},
  title        = {Pith review of: Advancing Higgsino Searches by Integrating ML for Boosted Object Tagging and Event Selection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QXEOEZNA}},
  note         = {Machine review of arXiv:2501.07491}
}
read the original abstract

Higgsinos near the TeV mass range are highly motivated as they offer an elegant solution to the naturalness problem in the Standard Model. Extensive searches for such higgsinos within the framework of General Gauge Mediation (GGM) have been conducted by both the ATLAS and CMS collaborations. However, the sensitivity of these searches in the hadronic channel remains limited, primarily due to the reliance on traditional substructure-based techniques for fat jet identification. In this work, we present a novel search strategy that leverages graph neural networks (GNNs) to improve the characterization of fat jets originating from W/Z/h bosons, top quarks, and QCD-initiated light quarks and gluons. The GNN scores, combined with a boosted decision tree (BDT) classifier, enhance signal-background discrimination, offering a significant improvement in sensitivity for higgsino searches at the LHC.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

85 extracted references · 31 canonical work pages

  1. [87]

    Hammad, S

    A. Hammad, S. Moretti, and M. Nojiri,Multi-scale cross-attention transformer encoder for event classification, JHEP 03 (2024) 144, [arXiv:2401.00452]

    arXiv 2024

  2. [89]

    Sahoo, R

    D. Sahoo, R. Sahu, and K. Ghosh,A Comprehensive Search for Leptoquarks Decaying into Top-τ Final States at the Future LHC, . – 24 –

    work page

  3. [52]

    Hayrapetyan et al.,Combined search for electroweak production of winos, binos, higgsinos, and sleptons in proton-proton collisions at s=13 TeV, Phys

    CMS Collaboration, A. Hayrapetyan et al.,Combined search for electroweak production of winos, binos, higgsinos, and sleptons in proton-proton collisions at s=13 TeV, Phys. Rev. D109 (2024), no. 11 112001, [arXiv:2402.01888]

    arXiv 2024

  4. [1]

    Sakai,Naturalness in supersymmetric GUTS, Z

    N. Sakai,Naturalness in supersymmetric GUTS, Z. Phys. C11 (1981) 153

    1981

  5. [2]

    Dimopoulos, S

    S. Dimopoulos, S. Raby, and F. Wilczek,Supersymmetry and the scale of unification, Phys. Rev. D24 (1981) 1681

    1981

  6. [3]

    L. E. Ibáñez and G. G. Ross,Low-energy predictions in supersymmetric grand unified theories, Phys. Lett. B105 (1981) 439

    1981

  7. [4]

    Dimopoulos and H

    S. Dimopoulos and H. Georgi,Softly broken supersymmetry and SU(5), Nucl. Phys. B 193 (1981) 150

    1981

  8. [5]

    Barbieri and G

    R. Barbieri and G. Giudice,Upper bounds on supersymmetric particle masses, Nucl. Phys. B 306 (1988) 63

    1988

Show all 85 references
  1. [6]

    Aad et al.,The quest to discover supersymmetry at the ATLAS experiment, arXiv:2403.02455

    A TLASCollaboration, G. Aad et al.,The quest to discover supersymmetry at the ATLAS experiment, arXiv:2403.02455

    arXiv

  2. [7]

    CMS Collaboration, Phenomenological MSSM interpretation of CMS searches in pp collisions at 13 TeV,

  3. [8]

    Adam and I

    W. Adam and I. Vivarelli,Status of searches for electroweak-scale supersymmetry after LHC Run 2, Int. J. Mod. Phys. A37 (2022), no. 02 2130022, [arXiv:2111.10180]

    2022 arXiv

  4. [13]

    ATLAS Collaboration,Searches for new phenomena in events with two leptons, jets, and missing transverse momentum in139fb−1 of √s = 13TeV pp collisions with the ATLAS detector, Eur. Phys. J. C83 (2023) 515, [arXiv:2204.13072]

    2023 arXiv

  5. [14]

    ATLAS Collaboration,Search for pair-produced higgsinos decaying via Higgs orZ bosons to final states containing a pair of photons and a pair ofb-jets with the ATLAS detector, arXiv:2404.01996

    arXiv

  6. [15]

    G. R. Farrar and P. Fayet,Phenomenology of the production, decay, and detection of new hadronic states associated with supersymmetry, Phys. Lett. B76 (1978) 575. – 18 –

    1978

  7. [16]

    Aad et al.,Searches for electroweak production of supersymmetric particles with compressed mass spectra in√s = 13 TeV pp collisions with the ATLAS detector, Phys

    A TLASCollaboration, G. Aad et al.,Searches for electroweak production of supersymmetric particles with compressed mass spectra in√s = 13 TeV pp collisions with the ATLAS detector, Phys. Rev. D101 (2020), no. 5 052005, [arXiv:1911.12606]

    2020 arXiv

  8. [17]

    CMS Collaboration, A. Tumasyan et al.,Search for supersymmetry in final states with two or three soft leptons and missing transverse momentum in proton-proton collisions at √s = 13 TeV, JHEP 04 (2022) 091, [arXiv:2111.06296]

    2022 arXiv

  9. [18]

    Y. A. Golfand and E. P. Likhtman,Extension of the Algebra of Poincare Group Generators and Violation of p Invariance, JETP Lett. 13 (1971) 323–326

    1971

  10. [19]

    D. V. Volkov and V. P. Akulov,Is the Neutrino a Goldstone Particle?, Phys. Lett. B 46 (1973) 109–110

    1973

  11. [20]

    Wess and B

    J. Wess and B. Zumino,Supergauge Transformations in Four-Dimensions, Nucl. Phys. B 70 (1974) 39–50

    1974

  12. [21]

    Dimopoulos and H

    S. Dimopoulos and H. Georgi,SUPERSYMMETRIC GUTS., in2nd Workshop on Grand Unification, pp. 285–296, 1981

    1981

  13. [22]

    M. B. Einhorn and D. R. T. Jones,The Weak Mixing Angle and Unification Mass in Supersymmetric SU(5), Nucl. Phys. B196 (1982) 475–488

    1982

  14. [23]

    J. R. Ellis, J. S. Hagelin, D. V. Nanopoulos, K. A. Olive, and M. Srednicki, Supersymmetric Relics from the Big Bang, Nucl. Phys. B238 (1984) 453–476

    1984

  15. [24]

    Goldberg,Constraint on the Photino Mass from Cosmology, Phys

    H. Goldberg,Constraint on the Photino Mass from Cosmology, Phys. Rev. Lett.50 (1983) 1419. [Erratum: Phys.Rev.Lett. 103, 099905 (2009)]

    1983

  16. [25]

    Mizuta and M

    S. Mizuta and M. Yamaguchi,Coannihilation effects and relic abundance of Higgsino dominant LSP(s), Phys. Lett. B298 (1993) 120–126, [hep-ph/9208251]

    1993 arXiv

  17. [26]

    Cirelli, A

    M. Cirelli, A. Strumia, and M. Tamburini,Cosmology and Astrophysics of Minimal Dark Matter, Nucl. Phys. B787 (2007) 152–175, [arXiv:0706.4071]

    2007 arXiv

  18. [27]

    J. L. Feng, K. T. Matchev, and F. Wilczek,Neutralino dark matter in focus point supersymmetry, Phys. Lett. B482 (2000) 388–399, [hep-ph/0004043]

    2000 arXiv

  19. [28]

    Hisano, S

    J. Hisano, S. Matsumoto, M. Nagai, O. Saito, and M. Senami,Non-perturbative effect on thermal relic abundance of dark matter, Phys. Lett. B646 (2007) 34–38, [hep-ph/0610249]

    2007 arXiv

  20. [29]

    Girardello and M

    L. Girardello and M. T. Grisaru,Soft Breaking of Supersymmetry, Nucl. Phys. B194 (1982) 65

    1982

  21. [30]

    H. P. Nilles,Dynamically Broken Supergravity and the Hierarchy Problem, Phys. Lett. B 115 (1982) 193

    1982

  22. [31]

    A. H. Chamseddine, R. L. Arnowitt, and P. Nath,Locally Supersymmetric Grand Unification, Phys. Rev. Lett.49 (1982) 970

    1982

  23. [32]

    P. Nath, R. L. Arnowitt, and A. H. Chamseddine,Gauge Hierarchy in Supergravity Guts, Nucl. Phys. B227 (1983) 121–133. – 19 –

    1983

  24. [33]

    Barbieri, S

    R. Barbieri, S. Ferrara, and C. A. Savoy,Gauge Models with Spontaneously Broken Local Supersymmetry, Phys. Lett. B119 (1982) 343

    1982

  25. [34]

    Cremmer, P

    E. Cremmer, P. Fayet, and L. Girardello,Gravity Induced Supersymmetry Breaking and Low-Energy Mass Spectrum, Phys. Lett. B122 (1983) 41

    1983

  26. [35]

    L. E. Ibanez,Locally Supersymmetric SU(5) Grand Unification, Phys. Lett. B118 (1982) 73–78

    1982

  27. [36]

    H. P. Nilles, M. Srednicki, and D. Wyler,Weak Interaction Breakdown Induced by Supergravity, Phys. Lett. B120 (1983) 346

    1983

  28. [37]

    Randall and R

    L. Randall and R. Sundrum,Out of this world supersymmetry breaking, Nucl. Phys. B 557 (1999) 79–118, [hep-th/9810155]

    1999 arXiv

  29. [38]

    G. F. Giudice, M. A. Luty, H. Murayama, and R. Rattazzi,Gaugino mass without singlets, JHEP 12 (1998) 027, [hep-ph/9810442]

    1998 arXiv

  30. [39]

    Dine and A

    M. Dine and A. E. Nelson,Dynamical supersymmetry breaking at low-energies, Phys. Rev. D 48 (1993) 1277–1287, [hep-ph/9303230]

    1993 arXiv

  31. [40]

    M. Dine, A. E. Nelson, and Y. Shirman,Low-energy dynamical supersymmetry breaking simplified, Phys. Rev. D51 (1995) 1362–1370, [hep-ph/9408384]

    1995 arXiv

  32. [41]

    M. Dine, A. E. Nelson, Y. Nir, and Y. Shirman,New tools for low-energy dynamical supersymmetry breaking, Phys. Rev. D53 (1996) 2658–2669, [hep-ph/9507378]

    1996 arXiv

  33. [42]

    Meade, N

    P. Meade, N. Seiberg, and D. Shih,General Gauge Mediation, Prog. Theor. Phys. Suppl. 177 (2009) 143–158, [arXiv:0801.3278]

    2009 arXiv

  34. [43]

    Buican, P

    M. Buican, P. Meade, N. Seiberg, and D. Shih,Exploring General Gauge Mediation, JHEP 03 (2009) 016, [arXiv:0812.3668]

    2009 arXiv

  35. [44]

    M. A. Ajaib, I. Gogoladze, F. Nasir, and Q. Shafi,Revisiting mGMSB in Light of a 125 GeV Higgs, Phys. Lett. B713 (2012) 462–468, [arXiv:1204.2856]

    2012 arXiv

  36. [45]

    A TLASCollaboration, G. Aad et al.,Search for supersymmetry in events with four or more charged leptons in 139 fb−1 of √s = 13 TeV pp collisions with the ATLAS detector, JHEP 07 (2021) 167, [arXiv:2103.11684]

    2021 arXiv

  37. [46]

    A TLASCollaboration, G. Aad et al.,Search for charginos and neutralinos in final states with two boosted hadronically decaying bosons and missing transverse momentum in pp collisions at √s = 13 TeV with the ATLAS detector, Phys. Rev. D 104 (2021), no. 11 112010, [arXiv:2108.07586]

    2021 arXiv

  38. [47]

    Aad et al.,Search for pair production of higgsinos in events with two Higgs bosons and missing transverse momentum in s=13 TeV pp collisions at the ATLAS experiment, Phys

    A TLASCollaboration, G. Aad et al.,Search for pair production of higgsinos in events with two Higgs bosons and missing transverse momentum in s=13 TeV pp collisions at the ATLAS experiment, Phys. Rev. D109 (2024), no. 11 112011, [arXiv:2401.14922]

    2024 arXiv

  39. [48]

    Aaboud et al.,Search for supersymmetry in events with four or more leptons in√s = 13 TeV pp collisions with ATLAS, Phys

    A TLASCollaboration, M. Aaboud et al.,Search for supersymmetry in events with four or more leptons in√s = 13 TeV pp collisions with ATLAS, Phys. Rev. D98 (2018), no. 3 032009, [arXiv:1804.03602]. – 20 –

    2018 arXiv

  40. [49]

    A TLASCollaboration, G. Aad et al.,Search for direct production of electroweakinos in final states with missing transverse momentum and a Higgs boson decaying into photons in pp collisions at√s = 13 TeV with the ATLAS detector, JHEP 10 (2020) 005, [arXiv:2004.10894]

    2020 arXiv

  41. [50]

    Aaboud et al.,Search for pair production of higgsinos in final states with at least threeb-tagged jets in√s = 13 TeV pp collisions using the ATLAS detector, Phys

    A TLASCollaboration, M. Aaboud et al.,Search for pair production of higgsinos in final states with at least threeb-tagged jets in√s = 13 TeV pp collisions using the ATLAS detector, Phys. Rev. D98 (2018), no. 9 092002, [arXiv:1806.04030]

    2018 arXiv

  42. [51]

    Tumasyan et al.,Search for electroweak production of charginos and neutralinos in proton-proton collisions at√s = 13 TeV, JHEP 04 (2022) 147, [arXiv:2106.14246]

    CMS Collaboration, A. Tumasyan et al.,Search for electroweak production of charginos and neutralinos in proton-proton collisions at√s = 13 TeV, JHEP 04 (2022) 147, [arXiv:2106.14246]

    2022 arXiv

  43. [53]

    CMS Collaboration, A. M. Sirunyan et al.,Search for electroweak production of charginos and neutralinos in multilepton final states in proton-proton collisions at√s = 13 TeV, JHEP 03 (2018) 166, [arXiv:1709.05406]

    2018 arXiv

  44. [54]

    CMS Collaboration, A. M. Sirunyan et al.,Search for supersymmetry in final states with two oppositely charged same-flavor leptons and missing transverse momentum in proton-proton collisions at√s = 13 TeV, JHEP 04 (2021) 123, [arXiv:2012.08600]

    2021 arXiv

  45. [55]

    CMS Collaboration, A. M. Sirunyan et al.,Search for supersymmetry with Higgs boson to diphoton decays using the razor variables at√s = 13 TeV, Phys. Lett. B 779 (2018) 166–190, [arXiv:1709.00384]

    2018 arXiv

  46. [56]

    CMS Collaboration, A. M. Sirunyan et al.,Search for Higgsino pair production inpp collisions at √s = 13 TeV in final states with large missing transverse momentum and two Higgs bosons decaying viaH → b¯b, Phys. Rev. D97 (2018), no. 3 032007, [arXiv:1709.04896]

    2018 arXiv

  47. [57]

    Tumasyan et al.,Search for higgsinos decaying to two Higgs bosons and missing transverse momentum in proton-proton collisions at√s = 13 TeV, JHEP 05 (2022) 014, [arXiv:2201.04206]

    CMS Collaboration, A. Tumasyan et al.,Search for higgsinos decaying to two Higgs bosons and missing transverse momentum in proton-proton collisions at√s = 13 TeV, JHEP 05 (2022) 014, [arXiv:2201.04206]

    2022 arXiv

  48. [58]

    Staub,SARAH 4 : A tool for (not only SUSY) model builders, Comput

    F. Staub,SARAH 4 : A tool for (not only SUSY) model builders, Comput. Phys. Commun. 185 (2014) 1773–1790, [arXiv:1309.7223]

    2014 arXiv

  49. [59]

    Staub,SARAH, arXiv:0806.0538

    F. Staub,SARAH, arXiv:0806.0538

    arXiv

  50. [60]

    Porod and F

    W. Porod and F. Staub,SPheno 3.1: Extensions including flavour, CP-phases and models beyond the MSSM, Comput. Phys. Commun.183 (2012) 2458–2469, [arXiv:1104.1573]

    2012 arXiv

  51. [61]

    Porod,SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders, Comput

    W. Porod,SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders, Comput. Phys. Commun. 153 (2003) 275–315, [hep-ph/0301101]. – 21 –

    2003 arXiv

  52. [62]

    Alwall, R

    J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro,The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, JHEP 07 ...

    2014 arXiv

  53. [63]

    B. Fuks, M. Klasen, D. R. Lamprea, and M. Rothering,Precision predictions for electroweak superpartner production at hadron colliders with Resummino, Eur. Phys. J. C 73 (2013) 2480, [arXiv:1304.0790]

    2013 arXiv

  54. [64]

    Muselli, M

    C. Muselli, M. Bonvini, S. Forte, S. Marzani, and G. Ridolfi,Top Quark Pair Production beyond NNLO, JHEP 08 (2015) 076, [arXiv:1505.02006]

    2015 arXiv

  55. [65]

    Catani, L

    S. Catani, L. Cieri, G. Ferrera, D. de Florian, and M. Grazzini,Vector boson production at hadron colliders: a fully exclusive QCD calculation at NNLO, Phys. Rev. Lett.103 (2009) 082001, [arXiv:0903.2120]

    2009 arXiv

  56. [66]

    Balossini, G

    G. Balossini, G. Montagna, C. M. Carloni Calame, M. Moretti, O. Nicrosini, F. Piccinini, M. Treccani, and A. Vicini,Combination of electroweak and QCD corrections to single W production at the Fermilab Tevatron and the CERN LHC, JHEP 01 (2010) 013, [arXiv:0907.0276]

    2010 arXiv

  57. [67]

    Broggio, A

    A. Broggio, A. Ferroglia, R. Frederix, D. Pagani, B. D. Pecjak, and I. Tsinikos, Top-quark pair hadroproduction in association with a heavy boson at NLO+NNLL including EW corrections, JHEP 08 (2019) 039, [arXiv:1907.04343]

    2019 arXiv

  58. [68]

    Kidonakis,Theoretical results for electroweak-boson and single-top production, PoS DIS2015 (2015) 170, [arXiv:1506.04072]

    N. Kidonakis,Theoretical results for electroweak-boson and single-top production, PoS DIS2015 (2015) 170, [arXiv:1506.04072]

    2015 arXiv

  59. [69]

    J. M. Campbell, R. K. Ellis, and C. Williams,Vector boson pair production at the LHC, JHEP 07 (2011) 018, [arXiv:1105.0020]

    2011 arXiv

  60. [70]

    Shen, R.-Y

    Y.-B. Shen, R.-Y. Zhang, W.-G. Ma, X.-Z. Li, Y. Zhang, and L. Guo,NLO QCD + NLO EW corrections toW ZZproductions with leptonic decays at the LHC, JHEP 10 (2015) 186, [arXiv:1507.03693]. [Erratum: JHEP 10, 156 (2016)]

    2015 arXiv

  61. [71]

    Sjöstrand, S

    T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands,An introduction to PYTHIA 8.2, Comput. Phys. Commun.191 (2015) 159–177, [arXiv:1410.3012]

    2015 arXiv

  62. [72]

    de Favereau, C

    DELPHES 3 Collaboration, J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi,DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP 02 (2014) 057, [arXiv:1307.6346]

    2014 arXiv

  63. [73]

    Cacciari, G

    M. Cacciari, G. P. Salam, and G. Soyez,The anti-kt jet clustering algorithm, JHEP 04 (2008) 063, [arXiv:0802.1189]

    2008 arXiv

  64. [74]

    Cacciari, G

    M. Cacciari, G. P. Salam, and G. Soyez,FastJet User Manual, Eur. Phys. J. C72 (2012) 1896, [arXiv:1111.6097]

    2012 arXiv

  65. [75]

    Aaboud et al.,Electron reconstruction and identification – 22 – in the ATLAS experiment using the 2015 and 2016 LHC proton-proton collision data at √s = 13 TeV, Eur

    A TLASCollaboration, M. Aaboud et al.,Electron reconstruction and identification – 22 – in the ATLAS experiment using the 2015 and 2016 LHC proton-proton collision data at √s = 13 TeV, Eur. Phys. J. C79 (2019), no. 8 639, [arXiv:1902.04655]

    2019 arXiv

  66. [76]

    Aaboud et al.,Search for supersymmetry in a final state containing two photons and missing transverse momentum in√s = 13 TeVpp collisions at the LHC using the ATLAS detector, Eur

    A TLASCollaboration, M. Aaboud et al.,Search for supersymmetry in a final state containing two photons and missing transverse momentum in√s = 13 TeVpp collisions at the LHC using the ATLAS detector, Eur. Phys. J. C76 (2016), no. 9 517, [arXiv:1606.09150]

    2016 arXiv

  67. [77]

    Aad et al.,Search for dark matter in association with an energetic photon inpp collisions at √s = 13 TeV with the ATLAS detector, JHEP 02 (2021) 226, [arXiv:2011.05259]

    A TLASCollaboration, G. Aad et al.,Search for dark matter in association with an energetic photon inpp collisions at √s = 13 TeV with the ATLAS detector, JHEP 02 (2021) 226, [arXiv:2011.05259]

    2021 arXiv

  68. [78]

    A TLASCollaboration, G. Aad et al.,Search for squarks and gluinos in final states with jets and missing transverse momentum using 139 fb−1 of √s =13 TeV pp collision data with the ATLAS detector, JHEP 02 (2021) 143, [arXiv:2010.14293]

    2021 arXiv

  69. [79]

    Chen and C

    T. Chen and C. Guestrin,Xgboost: A scalable tree boosting system, inProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, p. 785–794, ACM, Aug., 2016

    2016

  70. [80]

    Heinrich, M

    L. Heinrich, M. Feickert, G. Stark, and K. Cranmer,pyhf: pure-Python implementation of HistFactory statistical models, J. Open Source Softw.6 (2021), no. 58 2823

    2021

  71. [81]

    Feickert, L

    M. Feickert, L. Heinrich, and G. Stark,pyhf: a pure-Python statistical fitting library with tensors and automatic differentiation, PoS ICHEP2022 (11, 2022) 245, [arXiv:2211.15838]

    2022 arXiv

  72. [82]

    Cranmer, G

    ROOT Collaboration, K. Cranmer, G. Lewis, L. Moneta, A. Shibata, and W. Verkerke,HistFactory: A tool for creating statistical models for use with RooFit and RooStats,

  73. [83]

    Brun and F

    R. Brun and F. Rademakers,ROOT: An object oriented data analysis framework, Nucl. Instrum. Meth. A389 (1997) 81–86

    1997

  74. [84]

    Antcheva et al.,ROOT: A C++ framework for petabyte data storage, statistical analysis and visualization, Comput

    I. Antcheva et al.,ROOT: A C++ framework for petabyte data storage, statistical analysis and visualization, Comput. Phys. Commun.182 (2011) 1384–1385

    2011

  75. [85]

    CMS Collaboration, A. M. Sirunyan et al.,Search for physics beyond the standard model in multilepton final states in proton-proton collisions at√s = 13 TeV, JHEP 03 (2020) 051, [arXiv:1911.04968]

    2020 arXiv

  76. [86]

    A TLASCollaboration, Search for new phenomena in three- or four-lepton events in pp collisions at √s = 13 TeV with the ATLAS detector,

  77. [88]

    S. Gong, Q. Meng, J. Zhang, H. Qu, C. Li, S. Qian, W. Du, Z.-M. Ma, and T.-Y. Liu, An efficient Lorentz equivariant graph neural network for jet tagging, JHEP 07 (2022) 030, [arXiv:2201.08187]. – 23 –

    2022 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.