Agentic Re-Casting using Agentic Re-Simulations

Daniel Schiller; Nikita Schmal; Sascha Diefenbacher; Tilman Plehn

REVIEW 2 major objections 4 minor 1 cited by

Agentic Re-Casting using Agentic Re-Simulations

T0 review · 2 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read An agentic system with a physicist in the loop can add a new LHC measurement to a global SMEFT fit and tighten constraints on top-quark couplings.

desk verdict A genuinely useful agentic re-casting toolkit with an honest validation appendix, but the central SMEFT scan may be running in the exact silent-failure mode the paper itself documents. read the letter →

arxiv 2607.22813 v1 pith:PSWVUUMX submitted 2026-07-24 hep-ph

Sascha Diefenbacher , Tilman Plehn , Daniel Schiller , Nikita Schmal This is my paper · ORCID

classification hep-ph

keywords agenticAIanalysisre-castingSMEFTSFittertopquarksectorttZproductionrepeatablebenchmarksilentfailuremodes

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 shows that the months-long expert workflow of LHC analysis re-casting—extracting a published measurement, re-simulating it under a new theory hypothesis, and refitting a global likelihood—can be carried out by an LLM agent system supervised by a physicist. It demonstrates this with SFitter's global top-sector SMEFT fit: the agents select and extract an ATLAS ttZ differential measurement, re-simulate it at NLO, scan 21 dimension-six operators, assemble the datacard, and run the global analysis, which tightens the bounds on the top-electroweak Wilson coefficients by 34–42%. The paper also reports a blind benchmark in which six independent agent runs recover the Wilson coefficients of an injected coloron signal, consistent with the truth within statistical fluctuations. If this holds, published LHC measurements could be folded into global fits quickly, reproducibly, and with a physicist retaining control at every step.

What carries the argument

SFitterAgents, an agentic system built on MadAgents.v3, whose orchestrator routes each query to specialized consultant, worker, and reviewer subagents. Its operating principles—source grounding in the locally installed code, lasting memory records, completion-vs-correctness checks, recorded confidence, and adversarial review—are the mechanism that keeps silent simulation failures from corrupting the physics. The re-casting chain itself is carried by four steps: measurement extraction, SM re-simulation, SMEFT scan with per-bin kappa extraction (the linear and quadratic dependence of each bin on the Wilson coefficients), and SFitter likelihood construction with the physicist validating each st

What would settle it

Re-run the SMEFT scan for the pT(Z) and m(ttZ) observables using the two settings that pass the Appendix B silent-failure test (adjusted truncation and recomputed top width), extract the kappa parameters, and redo the global fit; if the profiled constraints in Figure 5 change materially—in particular the 34–42% tightening or the large negative Cφt shift—the agentic re-casting result as presented is not reproducible.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that SFitterAgents—built on the MadAgents.v3 consultant architecture—can perform the complete re-casting chain for a new measurement: it picks the ttZ measurement, extracts per-bin values and uncertainties, re-simulates the SM signal at parton and particle level, scans the SMEFT Wilson coefficients to extract the per-bin kappa parametrization, validates and assembles the SFitter datacard, and runs the exclusive-likelihood global fit. Adding the normalized pT(Z) spectrum tightens the profiled constraints on Cφt, C−φQ, CtZ and CtW by 34–42%, while m(ttZ) alone gives 19% on CtZ; the agent flags a 3σ underfluctuation in one pT(Z) bin that pulls Cφt to larg

Load-bearing premise

The re-casting demonstration assumes the SMEFTatNLO simulations for the new ttZ measurement correctly handle MadGraph's perturbative truncation of dimension-six operators and recompute the top width; the paper's own Appendix B silent-failure test shows this setup is answered correctly in only 0–2 of 10 runs, and no check confirms the actual scan avoided that failure mode.

Editorial extensions

If this is right

Adding the normalized pT(Z) ttZ spectrum to the top-sector global fit tightens the profiled constraints on the top-electroweak operators by 34–42%, and no existing bound is loosened.
The public likelihood with 276 nuisance parameters can be folded into the fit for the statistics-dominated ttZ measurement with results nearly identical to simpler per-bin uncertainty treatments; the same machinery will matter once systematics-dominated analyses are re-cast.
Six independent agent runs on blind coloron datasets reproduce the injected Wilson coefficients, with marginal likelihoods centered on the truth, establishing a repeatable benchmark for agentic re-casting.
The documented workflow structure allows an agent to reproduce a previous global analysis exactly, making agent-run fits auditable.
The four-step re-casting workflow and the agentic interface generalize beyond SFitter and beyond the top sector, applying to any simulation tool and any global analysis framework.

Reading between the lines

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

If the workflow scales as claimed, the same pipeline could maintain a continuously updated global SMEFT fit throughout the HL-LHC run, folding in every new differential measurement with a public likelihood as it appears.
A natural next step is to instrument the SMEFT scan so that the silent-failure checks from Appendix B (dimension-six truncation handling and top-width recomputation) run automatically before the kappa parameters are extracted; the paper's own validation shows that setup is exactly the case its agents answer correctly only 0–2 times out of 10.
The benchmark's observed breakdown of the dimension-six description near the coloron pole suggests an extension: at high invariant mass the workflow could match to UV-complete models directly instead of SMEFT, using the same agentic re-simulation chain.
Agent-driven re-casting could also serve as an automated new-physics scanner: bins that pull Wilson coefficients far from the SM, like the flagged 3σ pT(Z) bin, are surfaced to the physicist as candidate signals rather than being averaged away.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The real news here is the tooling. MadAgents.v3 with its consultant architecture and source-grounding rule is a step up from the earlier versions, and the SFitterAgents wrapper makes a genuine months-long SFitter task into a supervised workflow. The coloron injection benchmark is the most valuable part: it gives an external truth value, ships the simulation setup, and the repeated runs mostly land around the injected coefficients. The paper also deserves credit for printing its failures in Table 7 instead of hiding them. That is real evidence, and it should count.

Now the soft spots, in proportion. The one that matters is the SMEFT setup test in Appendix B. Table 7 shows no configuration answers the SMEFTatNLO question more than 2 out of 10 times; the warm setup gets 0/10. The paper says so itself: "none of the configurations answers it reliably." Yet the central demonstration in Sec. 4.3 builds the kappa parameterization of the ttZ measurement from SMEFTatNLO scans of 21 Wilson coefficients, and nowhere in that section is there a check that the scan avoided the two traps the Appendix identifies: MadGraph's tree-level truncation dropping the SM amplitude and the dipole, and the need to recompute the top width. If the scan ran with default settings, the kappa shapes could be wrong, and the claimed global constraints in Figures 4–5 would not be trustworthy. The reader's stress-test note lands here and it is load-bearing, not a nitpick.

Next, the coloron benchmark's grading is done by Claude Opus 4.8, and the "warm" configuration is trained on the same class of silent-failure lessons it is later tested on. That limits the benchmark's claim to generalizable reliability, though it does not invalidate what is shown. Run 2's outlier is explained away with narrative judgment rather than a quantitative criterion. And the claim that the new global analysis "reproduces previous results" would be stronger with an explicit side-by-side against the earlier SFitter fits from Refs. [9,13]; the paper gestures at this but does not show the figure.

None of this is a fatal problem. The architecture is sound, the benchmark is thoughtful, and the failure reporting is unusually honest. The missing piece is verification that the actual production scan in Sec. 4.3 is clean. That takes a commit hash, an explicit comparison to the previous fit, and a rerun of the SMEFT scan with the silent-failure fixes applied.

This paper deserves a serious referee. The weaknesses are concrete and addressable, and the tool-building contribution is real. I would bring it to a reading group and would cite it, but I would not yet use its SFitter constraints as a baseline for physics conclusions.

Referee Report

2 major / 4 minor

Summary. The paper presents MadAgents.v3, a consultant-based agentic layer for MadGraph, and SFitterAgents, an agentic interface to the SFitter global-fitting framework. The central demonstration is a physicist-in-the-loop re-casting exercise: the agents select the ATLAS ttZ measurement (arXiv:2312.04450), re-simulate the SM signal at parton and particle level, run SMEFTatNLO simulations with 21 Wilson coefficients, build κ parameterizations for pT(Z) and m(ttZ), and add these to the global top-sector SMEFT analysis, reproducing previous constraints and tightening them. Validation consists of five 'silent failure' tests (App. B) and a repeatable coloron-injection benchmark with six pseudo-datasets (App. C).

Significance. The paper has real strengths: App. C is a partly independent validation (coloron UV model, Eq. 17), the matching is checked against full coloron samples in Fig. 8, and the reproducible documentation structure plus Table 8 are concrete assets. App. B is unusually candid about hard failures. However, the main re-casting claim is not yet fully supported: the SMEFTatNLO setup used in Sec. 4.3 is exactly the class for which Table 7 shows all agent configurations fail most of the time (SMEFT setup: 0-2/10, warm 0/10), and the paper does not show that the actual scan avoided the truncation and top-width failure modes. Because the κ parametrization feeds the global likelihood and drives Figs. 4-5, this is a load-bearing gap. The central claim is defensible and the gap appears fixable, but requires additional evidence.

major comments (2)

[Sec. 4.3 and App. B, Table 7] Table 7 reports that the SMEFT setup question — correct perturbative truncation of SMEFTatNLO and recomputation of the top width for a dipole-modified decay — is answered correctly 0/10 times by the warm configuration and at most 2/10 by any configuration; the text states 'none of the configurations answers it reliably.' Section 4.3 then builds the full κ parametrization of the new ttZ measurement from SMEFTatNLO runs with 21 Wilson coefficients, and these κ shapes feed the global SFitter likelihood behind Figs. 4–5 and the paper's central claim. The paper does not show that the actual Sec. 4.3 campaign avoided the two failure modes (default tree-level truncation dropping the SM amplitude and dipole operator; inconsistent top width). A reviewer output, a Feynman-diagram check, an independent cross-check of one κ bin, or a statement from the human supervisor is required to establish that
[App. B (validation procedure) and Sec. 5] App. B's validation is self-referential in two ways that matter for the headline claim that MadAgents.v3 prevents silent failures. The grading is performed by an LLM agent (Claude Opus 4.8), and the warm configuration is trained on the same kind of silent-failure lessons on which it is tested. Table 7 gives no information on the grader's false-positive/negative rate, and for the SMEFT row all three configurations score 0–2/10. The paper should (i) report a human re-scoring of at least the SMEFT-question runs and (ii) soften the Sec. 5 statement that the workflow can be expanded 'with no risk concerning the quality of the results', which Table 7 does not support.

minor comments (4)

[Sec. 4.3, p. 13] The selected option 'NLO parton+reuse κ for particle plots' reuses parton-level κ shapes for particle-level plots. Since Sec. 4.4 uses parton-level data for the global fit, this does not affect the main result, but Fig. 3 should state this explicitly.
[Sec. 2.2, Eq. (3)] The correlation matrix sets ρ_ij=0.99 for all systematics pairs. This is a regularized full-correlation approximation, not exact full correlation; a sentence explaining the choice and any sensitivity test would help.
[App. C, Eq. (17)] The injected truth is defined by tree-level matching; the text already says higher-order matching would be more precise. Please add a sentence in the benchmark summary marking that the recovery test validates the tree-level matching value, not a full higher-order SMEFT prediction.
[Sec. 4.3, user prompt] Typo: 'out global analysis' should be 'our global analysis'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SFitter/MadGraph chain and the external coloron closure test give the central claim independent content; the App. B SMEFT silent-failure gap is a robustness risk, not a circular reduction.

full rationale

The derivation chain is: (i) extract ATLAS ttZ data; (ii) re-simulate the SM signal with MadGraph at NLO; (iii) generate SMEFTatNLO scans for the chosen Wilson coefficients and extract per-bin kappa responses; (iv) assemble an SFitter datacard and run the global likelihood. No step defines a Wilson coefficient or kappa in terms of the global-fit output: the kappa shapes come from simulation, and the ATLAS data enter only as comparison data in the likelihood. The claimed improvements are therefore a genuine theory-vs-data update, not a tautology. The strongest independent check is App. C: a coloron model with fixed parameters (Mc=3.75 TeV, tan(theta)=2.1, Gamma_c=1.26 TeV) is integrated out to give the tree-level relation c8/Lambda^2 = -g_c^2/M_c^2 = -0.40/TeV^2 (Eq. 17), and six Poisson-bootstrapped pseudo-datasets are fitted with SFitterAgents, recovering the injected Wilson coefficients and AC=0. This closure test is external to the fitted values and gives the central agentic claim independent content. Self-citations such as MadAgents [20], the previous top-SMEFT analysis [13], and SFitter [36-38] document the tools and baselines being benchmarked; they are not invoked as an unverified uniqueness theorem, and the paper grounds claims in the locally installed MadGraph/SFitter code. App. B does state a serious validation gap: 'The SMEFT question is the sole exception... none of the configurations answers it reliably' (Table 7: warm 0/10), and Sec. 4.3 does not report a check that its SMEFTatNLO scan avoided MadGraph's tree-level truncation and top-width recomputation issue. That is a correctness/robustness risk, not a circularity: the paper never shows that the Sec. 4.3 kappa values were set equal to that failure mode, and the coloron benchmark provides an independent success case. I therefore find no significant circularity.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

No invented entities: the only BSM object, the coloron, is taken from prior literature (Ref. [112]) as a test signal, not proposed as new physics. The free parameters are benchmark design choices and an inherited likelihood regularization, not fit results.

free parameters (2)

coloron benchmark parameters (M_c, tan θ, Γ_c) = M_c=3.75 TeV, tan θ=2.1, Γ_c=1.26 TeV
Chosen by hand in Eq. (15) so the coloron is detectable and the SMEFT approximation holds; these values set the injected-truth Wilson coefficient in Eq. (17), so the benchmark's expected outcome depends on them, but they are inputs, not fit outputs.
Correlation-matrix regularization ρ_ij=0.99 = 0.99
Ad hoc constant in Eq. (3) ensuring invertibility of the systematic-correlation matrix; inherited from the SFitter framework, not fitted here, but a user choice affecting the likelihood.

assumptions (5)

domain assumption Dimension-6 SMEFT truncation (with linear+quadratic terms) is the correct framework for interpreting LHC top-sector data; dimension-8 operators are not systematically included.
Sec. 2.1 states dimension-8 operators would exceed current data sensitivity and challenge the likelihood construction; the central re-casting result is phrased entirely in this truncated EFT.
domain assumption U(2) flavor symmetry on first two generations and zero light-quark masses (Eq. 12) define the 22-operator top-sector basis.
Sec. 2.3; the number of operators and their naming, which the agents are built around, follow from this symmetry.
domain assumption Gaussian approximation for the top-sector likelihood (Eqs. 9-10) is valid because signals are large and backgrounds negligible.
Sec. 2.2; the entire top-sector fit in the paper uses this simplified profile likelihood.
ad hoc to paper Tree-level coloron-to-SMEFT matching with a single Wilson coefficient for six color-octet operators (Eq. 17) is the correct injected truth for the App. C benchmark.
The paper states matching 'could be done more precisely at higher-order perturbation theory, but this approximate result is sufficient to check the numerical SFitter result.' The benchmark's passing or failing is judged against this self-defined truth.
domain assumption Public likelihood nuisance parameters (276 for the ttZ measurement) can be grouped into SFitter's correlation structure without loss.
Sec. 4.5; the comparison of uncertainty treatments assumes the profiled-likelihood extraction preserves the relevant correlations.

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Cite this review

Pith. "Pith review of Agentic Re-Casting using Agentic Re-Simulations." pith.science (2026). https://pith.science/paper/PSWVUUMX

@misc{pith2026260722813,
  author       = {Pith},
  title        = {Pith review of: Agentic Re-Casting using Agentic Re-Simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PSWVUUMX}},
  note         = {Machine review of arXiv:2607.22813}
}

read the original abstract

Analysis re-casting at the LHC is highly standardized and nevertheless requires resources, time, and physics input. Building on the new MadAgents.v3, we show how a global SFitter analysis can be updated by an agentic system with a physicist in the loop. The agentic interface allows us to make the advanced SFitter methodology available to a wider audience. All physical and technical aspects of this agentic re-casting study can be trivially generalized beyond SFitter.

Figures

Figures reproduced from arXiv: 2607.22813 by the authors.

**Figure 2.** Comparison of simulation vs data at parton level (left) and particle level [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

**Figure 3.** Effect of a selection of Wilson coefficients chosen to deviate by 3 [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Profiled constraints on Wilson coefficients from the original dataset and [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

**Figure 5.** Figure 5: Profiled constraints before and after adding the [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]

**Figure 6.** Figure 6: Effect of correlations between systematics on the profiled global analysis [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

**Figure 7.** Figure 7: Difference between implementing a single total uncertainty, estimating [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

**Figure 8.** Figure 8: Three-way SMEFT-matching validation in m(t¯t), comparing the Standard Model, SM + dimension-six SMEFT at linear order, SM + SMEFT at linear and quadratic order, and the full coloron sample, each with per-curve MC statistical band. 23 [PITH_FULL_IMAGE:figures/full_fig_…

**Figure 9.** Figure 9: Joint profile likelihoods of the six repeated global analyses, one curve per [PITH_FULL_IMAGE:figures/full_fig_p025_9.png]

**Figure 10.** Figure 10: Marginalized likelihoods of the six runs and the eight Wilson coefficients, [PITH_FULL_IMAGE:figures/full_fig_p026_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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Semi-visible jets, energy-based models, and self-supervision

Favaro, Luigi and Kr. Semi-visible jets, energy-based models, and self-supervision. SciPost Phys. 2025. doi:10.21468/SciPostPhys.18.2.042. arXiv:2312.03067

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Kicking it off(-shell) with direct diffusion

Butter, Anja and Jezo, Tomas and Klasen, Michael and Kuschick, Mathias and Palacios Schweitzer, Sofia and Plehn, Tilman. Kicking it off(-shell) with direct diffusion. SciPost Phys. Core. 2024. doi:10.21468/SciPostPhysCore.7.3.064. arXiv:2311.17175

2024 arXiv
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The MadNIS reloaded

Heimel, Theo and Huetsch, Nathan and Maltoni, Fabio and Mattelaer, Olivier and Plehn, Tilman and Winterhalder, Ramon. The MadNIS reloaded. SciPost Phys. 2024. doi:10.21468/SciPostPhys.17.1.023. arXiv:2311.01548

2024 arXiv
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Precision-machine learning for the matrix element method

Heimel, Theo and Huetsch, Nathan and Winterhalder, Ramon and Plehn, Tilman and Butter, Anja. Precision-machine learning for the matrix element method. SciPost Phys. 2024. doi:10.21468/SciPostPhys.17.5.129. arXiv:2310.07752

2024 arXiv
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Returning CP-observables to the frames they belong

Ackerschott, Jona and Barman, Rahool Kumar and Gon c alves, Dorival and Heimel, Theo and Plehn, Tilman. Returning CP-observables to the frames they belong. SciPost Phys. 2024. doi:10.21468/SciPostPhys.17.1.001. arXiv:2308.00027

2024 arXiv
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How to understand limitations of generative networks

Das, Ranit and Favaro, Luigi and Heimel, Theo and Krause, Claudius and Plehn, Tilman and Shih, David. How to understand limitations of generative networks. SciPost Phys. 2024. doi:10.21468/SciPostPhys.16.1.031. arXiv:2305.16774

2024 arXiv
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Jet diffusion versus JetGPT Modern networks for the LHC

Butter, Anja and Huetsch, Nathan and Palacios Schweitzer, Sofia and Plehn, Tilman and Sorrenson, Peter and Spinner, Jonas. Jet diffusion versus JetGPT Modern networks for the LHC. SciPost Phys. Core. 2025. doi:10.21468/SciPostPhysCore.8.1.026. arXiv:2305.10475

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and Favaro, Luigi and Feiden, Friedrich and Modak, Tanmoy and Plehn, Tilman

Dillon, Barry M. and Favaro, Luigi and Feiden, Friedrich and Modak, Tanmoy and Plehn, Tilman. Anomalies, representations, and self-supervision. SciPost Phys. Core. 2024. doi:10.21468/SciPostPhysCore.7.3.056. arXiv:2301.04660

2024 arXiv
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New Theory Paradigms at the LHC

M. New Theory Paradigms at the LHC. 2023. doi:10.1142/9789811280184_0001

2023 doi
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and Plehn, Tilman and Vogel, Lorenz

Butter, Anja and Dillon, Barry M. and Plehn, Tilman and Vogel, Lorenz. Performance versus resilience in modern quark-gluon tagging. SciPost Phys. Core. 2023. doi:10.21468/SciPostPhysCore.6.4.085. arXiv:2212.10493

2023 arXiv
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MadNIS - Neural multi-channel importance sampling

Heimel, Theo and Winterhalder, Ramon and Butter, Anja and Isaacson, Joshua and Krause, Claudius and Maltoni, Fabio and Mattelaer, Olivier and Plehn, Tilman. MadNIS - Neural multi-channel importance sampling. SciPost Phys. 2023. doi:10.21468/SciPostPhys.15.4.141. arXiv:2212.06172

2023 arXiv
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Modern Machine Learning for LHC Physicists

Plehn, Tilman and Butter, Anja and Dillon, Barry and Heimel, Theo and Krause, Claudius and Winterhalder, Ramon. Modern Machine Learning for LHC Physicists. 2022. arXiv:2211.01421

2022 arXiv
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Ghosh, Aishik and Nachman, Benjamin and Plehn, Tilman and Shire, Lily and Tait, Tim M. P. and Whiteson, Daniel. Statistical patterns of theory uncertainties. SciPost Phys. Core. 2023. doi:10.21468/SciPostPhysCore.6.2.045. arXiv:2210.15167

2023 arXiv
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Cornering extended Starobinsky inflation with CMB and SKA

Modak, Tanmoy and R. Cornering extended Starobinsky inflation with CMB and SKA. SciPost Phys. 2023. doi:10.21468/SciPostPhys.15.2.047. arXiv:2210.05698

2023 arXiv
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Maltoni, F. and others. TF07 Snowmass Report: Theory of Collider Phenomena. 2022. doi:10.2172/1898811. arXiv:2210.02591

2022 arXiv
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Two invertible networks for the matrix element method

Butter, Anja and Heimel, Theo and Martini, Till and Peitzsch, Sascha and Plehn, Tilman. Two invertible networks for the matrix element method. SciPost Phys. 2023. doi:10.21468/SciPostPhys.15.3.094. arXiv:2210.00019

2023 arXiv
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To profile or to marginalize - A SMEFT case study

Brivio, Ilaria and Bruggisser, Sebastian and Elmer, Nina and Geoffray, Emma and Luchmann, Michel and Plehn, Tilman. To profile or to marginalize - A SMEFT case study. SciPost Phys. 2024. doi:10.21468/SciPostPhys.16.1.035. arXiv:2208.08454

2024 arXiv
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Hazma meets HERWIG4DM: precision gamma-ray, neutrino, and positron spectra for light dark matter

Coogan, Adam and Morrison, Logan and Plehn, Tilman and Profumo, Stefano and Reimitz, Peter. Hazma meets HERWIG4DM: precision gamma-ray, neutrino, and positron spectra for light dark matter. JCAP. 2022. doi:10.1088/1475-7516/2022/11/033. arXiv:2207.07634

2022 arXiv
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Loop amplitudes from precision networks

Badger, Simon and Butter, Anja and Luchmann, Michel and Pitz, Sebastian and Plehn, Tilman. Loop amplitudes from precision networks. SciPost Phys. Core. 2023. doi:10.21468/SciPostPhysCore.6.2.034. arXiv:2206.14831

2023 arXiv
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and Favaro, Luigi and Plehn, Tilman and Sorrenson, Peter and Kr

Dillon, Barry M. and Favaro, Luigi and Plehn, Tilman and Sorrenson, Peter and Kr. A normalized autoencoder for LHC triggers. SciPost Phys. Core. 2023. doi:10.21468/SciPostPhysCore.6.4.074. arXiv:2206.14225

2023
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Campbell, J. M. and others. Event generators for high-energy physics experiments. SciPost Phys. 2024. doi:10.21468/SciPostPhys.16.5.130. arXiv:2203.11110

2024 arXiv
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Theory, phenomenology, and experimental avenues for dark showers: a Snowmass 2021 report

Albouy, Guillaume and others. Theory, phenomenology, and experimental avenues for dark showers: a Snowmass 2021 report. Eur. Phys. J. C. 2022. doi:10.1140/epjc/s10052-022-11048-8. arXiv:2203.09503

2021 arXiv
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Jets and Jet Substructure at Future Colliders

Bonilla, Johan and others. Jets and Jet Substructure at Future Colliders. Front. in Phys. 2022. doi:10.3389/fphy.2022.897719. arXiv:2203.07462

2022
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Machine learning and LHC event generation

Badger, Simon and others. Machine learning and LHC event generation. SciPost Phys. 2023. doi:10.21468/SciPostPhys.14.4.079. arXiv:2203.07460

2023 arXiv
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Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

Butter, Anja and Diefenbacher, Sascha and Kasieczka, Gregor and Nachman, Benjamin and Plehn, Tilman and Shih, David and Winterhalder, Ramon. Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows. SciPost Phys. 2022. doi:10.21468/SciPostPhys.13.4.087. a...

2022 arXiv
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Calomplification the power of generative calorimeter models

Bieringer, Sebastian and Butter, Anja and Diefenbacher, Sascha and Eren, Engin and Gaede, Frank and Hundhausen, Daniel and Kasieczka, Gregor and Nachman, Benjamin and Plehn, Tilman and Trabs, Mathias. Calomplification the power of generative calorimeter models. JINST. 2022. do...

2022 arXiv
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and Finke, Thorben and Kr

Buss, Thorsten and Dillon, Barry M. and Finke, Thorben and Kr. What's anomalous in LHC jets?. SciPost Phys. 2023. doi:10.21468/SciPostPhys.15.4.168. arXiv:2202.00686

2023 arXiv
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and Kerner, Matthias and Butter, Anja and Heinrich, Gudrun and Plehn, Tilman

Winterhalder, Ramon and Magerya, Vitaly and Villa, Emilio and Jones, Stephen P. and Kerner, Matthias and Butter, Anja and Heinrich, Gudrun and Plehn, Tilman. Targeting multi-loop integrals with neural networks. SciPost Phys. 2022. doi:10.21468/SciPostPhys.12.4.129. arXiv:2112.09145

2022 arXiv
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Probing the Inflaton Potential with SKA

Modak, Tanmoy and Plehn, Tilman and R. Probing the Inflaton Potential with SKA. SciPost Phys. Core. 2022. doi:10.21468/SciPostPhysCore.5.3.037. arXiv:2112.09148

2022 arXiv
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Generative networks for precision enthusiasts

Butter, Anja and Heimel, Theo and Hummerich, Sander and Krebs, Tobias and Plehn, Tilman and Rousselot, Armand and Vent, Sophia. Generative networks for precision enthusiasts. SciPost Phys. 2023. doi:10.21468/SciPostPhys.14.4.078. arXiv:2110.13632

2023 arXiv
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Back to the formula - LHC edition

Butter, Anja and Plehn, Tilman and Soybelman, Nathalie and Brehmer, Johann. Back to the formula - LHC edition. SciPost Phys. 2024. doi:10.21468/SciPostPhys.16.1.037. arXiv:2109.10414

2024 arXiv
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and Kasieczka, Gregor and Olischlager, Hans and Plehn, Tilman and Sorrenson, Peter and Vogel, Lorenz

Dillon, Barry M. and Kasieczka, Gregor and Olischlager, Hans and Plehn, Tilman and Sorrenson, Peter and Vogel, Lorenz. Symmetries, safety, and self-supervision. SciPost Phys. 2022. doi:10.21468/SciPostPhys.12.6.188. arXiv:2108.04253

2022 arXiv
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From models to SMEFT and back?

Brivio, Ilaria and Bruggisser, Sebastian and Geoffray, Emma and Killian, Wolfgang and Kr. From models to SMEFT and back?. SciPost Phys. 2022. doi:10.21468/SciPostPhys.12.1.036. arXiv:2108.01094

2022 arXiv
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Unsupervised hadronic SUEP at the LHC

Barron, Jared and Curtin, David and Kasieczka, Gregor and Plehn, Tilman and Spourdalakis, Aris. Unsupervised hadronic SUEP at the LHC. JHEP. 2021. doi:10.1007/JHEP12(2021)129. arXiv:2107.12379

2021 arXiv
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Shared Data and Algorithms for Deep Learning in Fundamental Physics

Benato, Lisa and others. Shared Data and Algorithms for Deep Learning in Fundamental Physics. Comput. Softw. Big Sci. 2022. doi:10.1007/s41781-022-00082-6. arXiv:2107.00656

2022 arXiv
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and Plehn, Tilman and Sauer, Christof and Sorrenson, Peter

Dillon, Barry M. and Plehn, Tilman and Sauer, Christof and Sorrenson, Peter. Better Latent Spaces for Better Autoencoders. SciPost Phys. 2021. doi:10.21468/SciPostPhys.11.3.061. arXiv:2104.08291

2021 arXiv
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Understanding Event-Generation Networks via Uncertainties

Bellagente, Marco and Haussmann, Manuel and Luchmann, Michel and Plehn, Tilman. Understanding Event-Generation Networks via Uncertainties. SciPost Phys. 2022. doi:10.21468/SciPostPhys.13.1.003. arXiv:2104.04543

2022 arXiv
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and Keilbach, Fabian and Plehn, Tilman and Kasieczka, Gregor and Whiteson, Daniel

Baldi, Pierre and Blecher, Lukas and Butter, Anja and Collado, Julian and Howard, Jessica N. and Keilbach, Fabian and Plehn, Tilman and Kasieczka, Gregor and Whiteson, Daniel. How to GAN Higher Jet Resolution. SciPost Phys. 2022. doi:10.21468/SciPostPhys.13.3.064. arXiv:2012.11944

2022 arXiv
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Measuring QCD Splittings with Invertible Networks

Bieringer, Sebastian and Butter, Anja and Heimel, Theo and H. Measuring QCD Splittings with Invertible Networks. SciPost Phys. 2021. doi:10.21468/SciPostPhys.10.6.126. arXiv:2012.09873

2021 arXiv
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How to GAN Event Unweighting

Backes, Mathias and Butter, Anja and Plehn, Tilman and Winterhalder, Ramon. How to GAN Event Unweighting. SciPost Phys. 2021. doi:10.21468/SciPostPhys.10.4.089. arXiv:2012.07873

2021 arXiv
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A final word on FCNC-Baryogenesis from two Higgs doublets

Hou, Wei-Shu and Modak, Tanmoy and Plehn, Tilman. A final word on FCNC-Baryogenesis from two Higgs doublets. SciPost Phys. 2021. doi:10.21468/SciPostPhys.10.6.150. arXiv:2012.03572

2021 arXiv
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Generative Networks for LHC events

Butter, Anja and Plehn, Tilman. Generative Networks for LHC events. 2020. arXiv:2008.08558

2020 arXiv
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GANplifying event samples

Butter, Anja and Diefenbacher, Sascha and Kasieczka, Gregor and Nachman, Benjamin and Plehn, Tilman. GANplifying event samples. SciPost Phys. 2021. doi:10.21468/SciPostPhys.10.6.139. arXiv:2008.06545

2021 arXiv
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Dark Matter EFT, the Third -- Neutrino WIMPs

Bischer, Ingolf and Plehn, Tilman and Rodejohann, Werner. Dark Matter EFT, the Third -- Neutrino WIMPs. SciPost Phys. 2021. doi:10.21468/SciPostPhys.10.2.039. arXiv:2008.04718

2021 arXiv
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Invertible Networks or Partons to Detector and Back Again

Bellagente, Marco and Butter, Anja and Kasieczka, Gregor and Plehn, Tilman and Rousselot, Armand and Winterhalder, Ramon and Ardizzone, Lynton and K. Invertible Networks or Partons to Detector and Back Again. SciPost Phys. 2020. doi:10.21468/SciPostPhys.9.5.074. arXiv:2006.06685

2020 arXiv
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Light Dark Matter Annihilation and Scattering in LHC Detectors

Bauer, Martin and Foldenauer, Patrick and Reimitz, Peter and Plehn, Tilman. Light Dark Matter Annihilation and Scattering in LHC Detectors. SciPost Phys. 2021. doi:10.21468/SciPostPhys.10.2.030. arXiv:2005.13551

2021 arXiv
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Per-Object Systematics using Deep-Learned Calibration

Kasieczka, Gregor and Luchmann, Michel and Otterpohl, Florian and Plehn, Tilman. Per-Object Systematics using Deep-Learned Calibration. SciPost Phys. 2020. doi:10.21468/SciPostPhys.9.6.089. arXiv:2003.11099

2020 arXiv
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How to GAN Event Subtraction

Butter, Anja and Plehn, Tilman and Winterhalder, Ramon. How to GAN Event Subtraction. SciPost Phys. Core. 2020. doi:10.21468/SciPostPhysCore.3.2.009. arXiv:1912.08824

2020 arXiv
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How to GAN away Detector Effects

Bellagente, Marco and Butter, Anja and Kasieczka, Gregor and Plehn, Tilman and Winterhalder, Ramon. How to GAN away Detector Effects. SciPost Phys. 2020. doi:10.21468/SciPostPhys.8.4.070. arXiv:1912.00477

2020 arXiv
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Hadronic Footprint of GeV-Mass Dark Matter

Plehn, Tilman and Reimitz, Peter and Richardson, Peter. Hadronic Footprint of GeV-Mass Dark Matter. SciPost Phys. 2020. doi:10.21468/SciPostPhys.8.6.092. arXiv:1911.11147

2020 arXiv
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O new physics, where art thou? A global search in the top sector

Brivio, Ilaria and Bruggisser, Sebastian and Maltoni, Fabio and Moutafis, Rhea and Plehn, Tilman and Vryonidou, Eleni and Westhoff, Susanne and Zhang, C. O new physics, where art thou? A global search in the top sector. JHEP. 2020. doi:10.1007/JHEP02(2020)131. arXiv:1910.03606

2020 arXiv
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Benchmarking simplified template cross sections in WH production

Brehmer, Johann and Dawson, Sally and Homiller, Samuel and Kling, Felix and Plehn, Tilman. Benchmarking simplified template cross sections in WH production. JHEP. 2019. doi:10.1007/JHEP11(2019)034. arXiv:1908.06980

2019 arXiv
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How to GAN LHC Events

Butter, Anja and Plehn, Tilman and Winterhalder, Ramon. How to GAN LHC Events. SciPost Phys. 2019. doi:10.21468/SciPostPhys.7.6.075. arXiv:1907.03764

2019 arXiv
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Proposal for the validation of Monte Carlo implementations of the standard model effective field theory

Maltoni, Fabio and others. Proposal for the validation of Monte Carlo implementations of the standard model effective field theory. 2019. arXiv:1906.12310

2019 arXiv
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and Plehn, Tilman and Romero, Alexis and Tait, Tim M

Lillard, Ben G. and Plehn, Tilman and Romero, Alexis and Tait, Tim M. P. Multi-scale Mining of Kinematic Distributions with Wavelets. SciPost Phys. 2020. doi:10.21468/SciPostPhys.8.3.043. arXiv:1906.10890

2020 arXiv
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CapsNets Continuing the Convolutional Quest

Diefenbacher, Sascha and Frost, Hermann and Kasieczka, Gregor and Plehn, Tilman and Thompson, Jennifer M. CapsNets Continuing the Convolutional Quest. SciPost Phys. 2020. doi:10.21468/SciPostPhys.8.2.023. arXiv:1906.11265

2020 arXiv
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Deep-Learning Jets with Uncertainties and More

Bollweg, Sven and Hau mann, Manuel and Kasieczka, Gregor and Luchmann, Michel and Plehn, Tilman and Thompson, Jennifer. Deep-Learning Jets with Uncertainties and More. SciPost Phys. 2020. doi:10.21468/SciPostPhys.8.1.006. arXiv:1904.10004

2020 arXiv
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The Machine Learning landscape of top taggers

Butter, Anja and others. The Machine Learning landscape of top taggers. SciPost Phys. 2019. doi:10.21468/SciPostPhys.7.1.014. arXiv:1902.09914

2019 arXiv
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Monte Carlo event generators for high energy particle physics event simulation

Alioli, Simone and others. Monte Carlo event generators for high energy particle physics event simulation. 2019. arXiv:1902.01674

2019 arXiv
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and others

Cepeda, M. and others. Report from Working Group 2 : Higgs Physics at the HL-LHC and HE-LHC. CERN Yellow Rep. Monogr. 2019. doi:10.23731/CYRM-2019-007.221. arXiv:1902.00134

2019 arXiv
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Quark-Gluon Tagging: Machine Learning vs Detector

Kasieczka, Gregor and Kiefer, Nicholas and Plehn, Tilman and Thompson, Jennifer M. Quark-Gluon Tagging: Machine Learning vs Detector. SciPost Phys. 2019. doi:10.21468/SciPostPhys.6.6.069. arXiv:1812.09223

2019 arXiv
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The Gauge-Higgs Legacy of the LHC Run II

Biek. The Gauge-Higgs Legacy of the LHC Run II. SciPost Phys. 2019. doi:10.21468/SciPostPhys.6.6.064. arXiv:1812.07587

2019 arXiv
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and others

Abada, A. and others. HE-LHC: The High-Energy Large Hadron Collider : Future Circular Collider Conceptual Design Report Volume 4. Eur. Phys. J. ST. 2019. doi:10.1140/epjst/e2019-900088-6

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Abada, A. and others. FCC-hh: The Hadron Collider : Future Circular Collider Conceptual Design Report Volume 3. Eur. Phys. J. ST. 2019. doi:10.1140/epjst/e2019-900087-0

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Abada, A. and others. FCC-ee: The Lepton Collider : Future Circular Collider Conceptual Design Report Volume 2. Eur. Phys. J. ST. 2019. doi:10.1140/epjst/e2019-900045-4

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Abada, A. and others. FCC Physics Opportunities : Future Circular Collider Conceptual Design Report Volume 1. Eur. Phys. J. C. 2019. doi:10.1140/epjc/s10052-019-6904-3

2019 doi
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The global Higgs picture at 27 TeV

Biek. The global Higgs picture at 27 TeV. SciPost Phys. 2019. doi:10.21468/SciPostPhys.6.2.024. arXiv:1811.08401

2019 arXiv
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QCD or What?

Heimel, Theo and Kasieczka, Gregor and Plehn, Tilman and Thompson, Jennifer M. QCD or What?. SciPost Phys. 2019. doi:10.21468/SciPostPhys.6.3.030. arXiv:1808.08979

2019 arXiv
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Higgs Physics: It ain't over till it's over

Dawson, Sally and Englert, Christoph and Plehn, Tilman. Higgs Physics: It ain't over till it's over. Phys. Rept. 2019. doi:10.1016/j.physrep.2019.05.001. arXiv:1808.01324

2019 arXiv
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Actual Physics behind Mono-X

Bernreuther, Elias and Horak, Jan and Plehn, Tilman and Butter, Anja. Actual Physics behind Mono-X. SciPost Phys. 2018. doi:10.21468/SciPostPhys.5.4.034. arXiv:1805.11637

2018 arXiv
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Dark Matter in Anomaly-Free Gauge Extensions

Bauer, Martin and Diefenbacher, Sascha and Plehn, Tilman and Russell, Michael and Camargo, Daniel A. Dark Matter in Anomaly-Free Gauge Extensions. SciPost Phys. 2018. doi:10.21468/SciPostPhys.5.4.036. arXiv:1805.01904

2018 arXiv
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and others

Barducci, D. and others. Interpreting top-quark LHC measurements in the standard-model effective field theory. 2018. arXiv:1802.07237

2018 arXiv
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Higgs boson pair production at future hadron colliders: From kinematics to dynamics

Gon c alves, Dorival and Han, Tao and Kling, Felix and Plehn, Tilman and Takeuchi, Michihisa. Higgs boson pair production at future hadron colliders: From kinematics to dynamics. Phys. Rev. D. 2018. doi:10.1103/PhysRevD.97.113004. arXiv:1802.04319

2018 arXiv
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Brehmer, Johann and Cranmer, Kyle and Kling, Felix and Tait, Tim M. P. and Plehn, Tilman. Better Higgs Measurements through Information Geometry. 53rd Rencontres de Moriond on QCD and High Energy Interactions. 2018

2018
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Dark Matter from Electroweak Single Top Production

Plehn, Tilman and Thompson, Jennifer and Westhoff, Susanne. Dark Matter from Electroweak Single Top Production. Phys. Rev. D. 2018. doi:10.1103/PhysRevD.98.015012. arXiv:1712.08065

2018 arXiv
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Tagging Jets in Invisible Higgs Searches

Biek. Tagging Jets in Invisible Higgs Searches. SciPost Phys. 2018. doi:10.21468/SciPostPhys.4.6.035. arXiv:1712.03973

2018 arXiv
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Brehmer, Johann and Kling, Felix and Plehn, Tilman and Tait, Tim M. P. Better Higgs-CP Tests Through Information Geometry. Phys. Rev. D. 2018. doi:10.1103/PhysRevD.97.095017. arXiv:1712.02350

2018 arXiv
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and Plehn, Tilman and Scherer, Michael M

Reichert, Manuel and Eichhorn, Astrid and Gies, Holger and Pawlowski, Jan M. and Plehn, Tilman and Scherer, Michael M. Probing baryogenesis through the Higgs boson self-coupling. Phys. Rev. D. 2018. doi:10.1103/PhysRevD.97.075008. arXiv:1711.00019

2018 arXiv
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Deep-learned Top Tagging with a Lorentz Layer

Butter, Anja and Kasieczka, Gregor and Plehn, Tilman and Russell, Michael. Deep-learned Top Tagging with a Lorentz Layer. SciPost Phys. 2018. doi:10.21468/SciPostPhys.5.3.028. arXiv:1707.08966

2018 arXiv
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Higgs factories: Higgsstrahlung versus W fusion

Lafaye, Remi and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk. Higgs factories: Higgsstrahlung versus W fusion. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.96.075044. arXiv:1706.02174

2017 arXiv
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Yet Another Introduction to Dark Matter : The Particle Physics Approach

Bauer, Martin and Plehn, Tilman. Yet Another Introduction to Dark Matter : The Particle Physics Approach. 2019. doi:10.1007/978-3-030-16234-4. arXiv:1705.01987

2019 arXiv
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Weak boson fusion at 100 TeV

Goncalves, Dorival and Plehn, Tilman and Thompson, Jennifer M. Weak boson fusion at 100 TeV. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.95.095011. arXiv:1702.05098

2017 arXiv
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Deep-learning Top Taggers or The End of QCD?

Kasieczka, Gregor and Plehn, Tilman and Russell, Michael and Schell, Torben. Deep-learning Top Taggers or The End of QCD?. JHEP. 2017. doi:10.1007/JHEP05(2017)006. arXiv:1701.08784

2017 arXiv
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Butter, Anja and Murgia, Simona and Plehn, Tilman and Tait, Tim M. P. Saving the MSSM from the Galactic Center Excess. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.96.035036. arXiv:1612.07115

2017 arXiv
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Better Higgs boson measurements through information geometry

Brehmer, Johann and Cranmer, Kyle and Kling, Felix and Plehn, Tilman. Better Higgs boson measurements through information geometry. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.95.073002. arXiv:1612.05261

2017 arXiv
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Validity of dark matter effective theory

Bauer, Martin and Butter, Anja and Desai, Nishita and Gonzalez-Fraile, Juan and Plehn, Tilman. Validity of dark matter effective theory. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.95.075036. arXiv:1611.09908

2017 arXiv
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LHC multijet events as a probe for anomalous dimension-six gluon interactions

Krauss, Frank and Kuttimalai, Silvan and Plehn, Tilman. LHC multijet events as a probe for anomalous dimension-six gluon interactions. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.95.035024. arXiv:1611.00767

2017 arXiv
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and others

de Florian, D. and others. Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector. CERN Yellow Rep. Monogr. 2017. doi:10.23731/CYRM-2017-002. arXiv:1610.07922

2017 arXiv
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When matching matters: Loop effects in Higgs effective theory

Freitas, Ayres and L \'o pez-Val, David and Plehn, Tilman. When matching matters: Loop effects in Higgs effective theory. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.94.095007. arXiv:1607.08251

2016 arXiv
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Maximizing the significance in Higgs boson pair analyses

Kling, Felix and Plehn, Tilman and Schichtel, Peter. Maximizing the significance in Higgs boson pair analyses. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.95.035026. arXiv:1607.07441

2017 arXiv
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Learning from a Higgs-like scalar resonance

Bauer, Martin and Butter, Anja and Gonzalez-Fraile, Juan and Plehn, Tilman and Rauch, Michael. Learning from a Higgs-like scalar resonance. Phys. Rev. D. 2017. doi:10.1103/PhysRevD.95.055011. arXiv:1607.04562

2017 arXiv
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and others

Contino, R. and others. Physics at a 100 TeV pp collider: Higgs and EW symmetry breaking studies. 2016. doi:10.23731/CYRM-2017-003.255. arXiv:1606.09408

2016 arXiv
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and others

Golling, T. and others. Physics at a 100 TeV pp collider: beyond the Standard Model phenomena. 2016. doi:10.23731/CYRM-2017-003.441. arXiv:1606.00947

2016 arXiv
[233]

The Higgs Portal and Cosmology

Assamagan, Ketevi and others. The Higgs Portal and Cosmology. 2016. arXiv:1604.05324

2016 arXiv
[234]

Butter, Anja and \'E boli, Oscar J. P. and Gonzalez-Fraile, J. and Gonzalez-Garcia, M. C. and Plehn, Tilman and Rauch, Michael. The Gauge-Higgs Legacy of the LHC Run I. JHEP. 2016. doi:10.1007/JHEP07(2016)152. arXiv:1604.03105

2016 arXiv
[235]

Hunting the Flavon

Bauer, Martin and Schell, Torben and Plehn, Tilman. Hunting the Flavon. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.94.056003. arXiv:1603.06950

2016 arXiv
[236]

Extending the limits of Higgs effective theory

Biek. Extending the limits of Higgs effective theory. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.94.055032. arXiv:1602.05202

2016 arXiv
[237]

Corbett, Tyler and Eboli, Oscar J. P. and Goncalves, Dorival and Gonzalez-Fraile, J. and Plehn, Tilman and Rauch, Michael. The Non-Linear Higgs Legacy of the LHC Run I. 2015. arXiv:1511.08188

2015 arXiv
[238]

Pushing Higgs Effective Theory to its Limits

Brehmer, Johann and Freitas, Ayres and Lopez-Val, David and Plehn, Tilman. Pushing Higgs Effective Theory to its Limits. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.93.075014. arXiv:1510.03443

2016 arXiv
[239]

Towards the Final Word on Neutralino Dark Matter

Bramante, Joseph and Desai, Nishita and Fox, Patrick and Martin, Adam and Ostdiek, Bryan and Plehn, Tilman. Towards the Final Word on Neutralino Dark Matter. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.93.063525. arXiv:1510.03460

2016 arXiv
[240]

and Plehn, Tilman and Reimitz, Peter and Schell, Torben and Shao, Hua-Sheng

Mangano, Michelangelo L. and Plehn, Tilman and Reimitz, Peter and Schell, Torben and Shao, Hua-Sheng. Measuring the Top Yukawa Coupling at 100 TeV. J. Phys. G. 2016. doi:10.1088/0954-3899/43/3/035001. arXiv:1507.08169

2016 arXiv
[241]

Invisible Higgs Decays to Hooperons in the NMSSM

Butter, Anja and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk and Henrot-Versill \'e , Sophie and Lafaye, R \'e mi. Invisible Higgs Decays to Hooperons in the NMSSM. Phys. Rev. D. 2016. doi:10.1103/PhysRevD.93.015011. arXiv:1507.02288

2016 arXiv
[242]

Supersymmetry

Bechtle, Philip and Plehn, Tilman and Sander, Christian. Supersymmetry. The Large Hadron Collider. Harvest of Run 1. 2015. doi:10.1007/978-3-319-15001-7_10. arXiv:1506.03091

2015 arXiv
[243]

Corbett, Tyler and Eboli, Oscar J. P. and Goncalves, Dorival and Gonzalez-Fraile, J. and Plehn, Tilman and Rauch, Michael. The Higgs Legacy of the LHC Run I. JHEP. 2015. doi:10.1007/JHEP08(2015)156. arXiv:1505.05516

2015 arXiv
[244]

Resonance Searches with an Updated Top Tagger

Kasieczka, Gregor and Plehn, Tilman and Schell, Torben and Strebler, Thomas and Salam, Gavin P. Resonance Searches with an Updated Top Tagger. JHEP. 2015. doi:10.1007/JHEP06(2015)203. arXiv:1503.05921

2015 arXiv
[245]

and Sondenheimer, Ren \'e

Eichhorn, Astrid and Gies, Holger and Jaeckel, Joerg and Plehn, Tilman and Scherer, Michael M. and Sondenheimer, Ren \'e. The Higgs Mass and the Scale of New Physics. JHEP. 2015. doi:10.1007/JHEP04(2015)022. arXiv:1501.02812

2015 arXiv
[246]

Lectures on LHC Physics

Plehn, Tilman. Lectures on LHC Physics. 2015. doi:10.1007/978-3-319-05942-6

2015 doi
[247]

and Martin, Adam and Ostdiek, Bryan and Plehn, Tilman and Schell, Torben and Takeuchi, Michihisa

Bramante, Joseph and Fox, Patrick J. and Martin, Adam and Ostdiek, Bryan and Plehn, Tilman and Schell, Torben and Takeuchi, Michihisa. Relic Neutralino Surface at a 100 TeV Collider. Phys. Rev. D. 2015. doi:10.1103/PhysRevD.91.054015. arXiv:1412.4789

2015 arXiv
[248]

Spying an invisible Higgs boson

Bernaciak, Catherine and Plehn, Tilman and Schichtel, Peter and Tattersall, Jamie. Spying an invisible Higgs boson. Phys. Rev. D. 2015. doi:10.1103/PhysRevD.91.035024. arXiv:1411.7699

2015 arXiv
[249]

Mass Effects in the Higgs-Gluon Coupling: Boosted vs Off-Shell Production

Buschmann, Malte and Goncalves, Dorival and Kuttimalai, Silvan and Schonherr, Marek and Krauss, Frank and Plehn, Tilman. Mass Effects in the Higgs-Gluon Coupling: Boosted vs Off-Shell Production. JHEP. 2015. doi:10.1007/JHEP02(2015)038. arXiv:1410.5806

2015 arXiv
[250]

Squark and gluino production cross sections in pp collisions at s = 13, 14, 33 and 100 TeV

Borschensky, Christoph and Kr. Squark and gluino production cross sections in pp collisions at s = 13, 14, 33 and 100 TeV. Eur. Phys. J. C. 2014. doi:10.1140/epjc/s10052-014-3174-y. arXiv:1407.5066

2014 arXiv
[251]

Automated third generation squark production to next-to-leading order

Goncalves, Dorival and Lopez-Val, David and Mawatari, Kentarou and Plehn, Tilman. Automated third generation squark production to next-to-leading order. Phys. Rev. D. 2014. doi:10.1103/PhysRevD.90.075007. arXiv:1407.4302

2014 arXiv
[252]

Resolving the Higgs-Gluon Coupling with Jets

Buschmann, Malte and Englert, Christoph and Goncalves, Dorival and Plehn, Tilman and Spannowsky, Michael. Resolving the Higgs-Gluon Coupling with Jets. Phys. Rev. D. 2014. doi:10.1103/PhysRevD.90.013010. arXiv:1405.7651

2014 arXiv
[253]

Higgs Physics

Plehn, Tilman. Higgs Physics. Lect. Notes Phys. 2015. doi:10.1007/978-3-319-05942-6_1

2015 doi
[254]

Polarized WW Scattering on the Higgs Pole

Brehmer, Johann and Jaeckel, Joerg and Plehn, Tilman. Polarized WW Scattering on the Higgs Pole. Phys. Rev. D. 2014. doi:10.1103/PhysRevD.90.054023. arXiv:1404.5951

2014 arXiv
[255]

and Freitas, A

Englert, C. and Freitas, A. and M. Precision Measurements of Higgs Couplings: Implications for New Physics Scales. J. Phys. G. 2014. doi:10.1088/0954-3899/41/11/113001. arXiv:1403.7191

2014 arXiv
[256]

and Plehn, Tilman and Ramsey-Musolf, Michael J

Buckley, Matthew R. and Plehn, Tilman and Ramsey-Musolf, Michael J. Top squark with mass close to the top quark. Phys. Rev. D. 2014. doi:10.1103/PhysRevD.90.014046. arXiv:1403.2726

2014 arXiv
[257]

Decoupling Theoretical Uncertainties from Measurements of the Higgs Boson

Cranmer, Kyle and Kreiss, Sven and Lopez-Val, David and Plehn, Tilman. Decoupling Theoretical Uncertainties from Measurements of the Higgs Boson. Phys. Rev. D. 2015. doi:10.1103/PhysRevD.91.054032. arXiv:1401.0080

2015 arXiv
[258]

Benchmarking an even better top tagger algorithm

Anders, Christoph and Bernaciak, Catherine and Kasieczka, Gregor and Plehn, Tilman and Schell, Torben. Benchmarking an even better top tagger algorithm. Phys. Rev. D. 2014. doi:10.1103/PhysRevD.89.074047. arXiv:1312.1504

2014 arXiv
[259]

Improving Higgs plus Jets analyses through Fox--Wolfram Moments

Bernaciak, Catherine and Mellado, Bruce and Plehn, Tilman and Schichtel, Peter and Ruan, Xifeng. Improving Higgs plus Jets analyses through Fox--Wolfram Moments. Phys. Rev. D. 2014. doi:10.1103/PhysRevD.89.053006. arXiv:1311.5891

2014 arXiv
[260]

and others

Altheimer, A. and others. Boosted Objects and Jet Substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012. Eur. Phys. J. C. 2014. doi:10.1140/epjc/s10052-014-2792-8. arXiv:1311.2708

2012 arXiv
[261]

Where boosted significances come from

Plehn, Tilman and Schichtel, Peter and Wiegand, Daniel. Where boosted significances come from. Phys. Rev. D. 2014. doi:10.1103/PhysRevD.89.054002. arXiv:1311.2591

2014 arXiv
[262]

Working Group Report: Higgs Boson

Dawson, Sally and others. Working Group Report: Higgs Boson. Snowmass 2013 : Snowmass on the Mississippi. 2013. arXiv:1310.8361

2013 arXiv
[263]

and Plehn, Tilman and Schell, Torben and Takeuchi, Michihisa

Buckley, Matthew R. and Plehn, Tilman and Schell, Torben and Takeuchi, Michihisa. Buckets of Higgs and Tops. JHEP. 2014. doi:10.1007/JHEP02(2014)130. arXiv:1310.6034

2014 arXiv
[264]

Constraining Supersymmetry using the relic density and the Higgs boson

Henrot-Versill \'e , Sophie and Lafaye, R \'e mi and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk and Plaszczynski, St \'e phane and Rouill \'e d'Orfeuil, Benjamin and Spinelli, Marta. Constraining Supersymmetry using the relic density and the Higgs boson. Phys. Rev. D. 2...

2014 arXiv
[265]

Measuring extended Higgs sectors as a consistent free couplings model

L \'o pez-Val, David and Plehn, Tilman and Rauch, Michael. Measuring extended Higgs sectors as a consistent free couplings model. JHEP. 2013. doi:10.1007/JHEP10(2013)134. arXiv:1308.1979

2013 arXiv
[266]

Looking for leptogluons

Goncalves-Netto, Dorival and Lopez-Val, David and Mawatari, Kentarou and Wigmore, Ioan and Plehn, Tilman. Looking for leptogluons. Phys. Rev. D. 2013. doi:10.1103/PhysRevD.87.094023. arXiv:1303.0845

2013 arXiv
[267]

and Plehn, Tilman and Takeuchi, Michihisa

Buckley, Matthew R. and Plehn, Tilman and Takeuchi, Michihisa. Buckets of Tops. JHEP. 2013. doi:10.1007/JHEP08(2013)086. arXiv:1302.6238

2013 arXiv
[268]

and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk

Klute, Markus and Lafaye, R. and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk. Measuring Higgs Couplings at a Linear Collider. EPL. 2013. doi:10.1209/0295-5075/101/51001. arXiv:1301.1322

2013 arXiv
[269]

Theory question for the Higgs sector

Plehn, Tilman. Theory question for the Higgs sector. EPJ Web Conf. 2013. doi:10.1051/epjconf/20134909004

2013
[270]

Higgs Quantum Numbers in Weak Boson Fusion

Englert, Christoph and Goncalves-Netto, Dorival and Mawatari, Kentarou and Plehn, Tilman. Higgs Quantum Numbers in Weak Boson Fusion. JHEP. 2013. doi:10.1007/JHEP01(2013)148. arXiv:1212.0843

2013 arXiv
[271]

Fox-Wolfram Moments in Higgs Physics

Bernaciak, Catherine and Buschmann, Malte Sean Andreas and Butter, Anja and Plehn, Tilman. Fox-Wolfram Moments in Higgs Physics. Phys. Rev. D. 2013. doi:10.1103/PhysRevD.87.073014. arXiv:1212.4436

2013 arXiv
[272]

Automated Squark and Gluino Production to Next-to-Leading Order

Gon c alves-Netto, Dorival and L \'o pez-Val, David and Mawatari, Kentarou and Plehn, Tilman and Wigmore, Ioan. Automated Squark and Gluino Production to Next-to-Leading Order. Phys. Rev. D. 2013. doi:10.1103/PhysRevD.87.014002. arXiv:1211.0286

2013 arXiv
[273]

MadGolem: automating NLO calculations for New Physics

Lopez-Val, David and Goncalves-Netto, Dorival and Plehn, Tilman and Mawatari, Kentarou and Wigmore, Ioan. MadGolem: automating NLO calculations for New Physics. PoS. 2012. doi:10.22323/1.151.0048. arXiv:1209.2797

2012 arXiv
[274]

Scaling Patterns for QCD Jets

Gerwick, Erik and Plehn, Tilman and Schumann, Steffen and Schichtel, Peter. Scaling Patterns for QCD Jets. JHEP. 2012. doi:10.1007/JHEP10(2012)162. arXiv:1208.3676

2012 arXiv
[275]

Tagging single Tops

Kling, Felix and Plehn, Tilman and Takeuchi, Michihisa. Tagging single Tops. Phys. Rev. D. 2012. doi:10.1103/PhysRevD.86.094029. arXiv:1207.4787

2012 arXiv
[276]

Higgs Couplings after the Discovery

Plehn, Tilman and Rauch, Michael. Higgs Couplings after the Discovery. EPL. 2012. doi:10.1209/0295-5075/100/11002. arXiv:1207.6108

2012 arXiv
[277]

From Jet Counting to Jet Vetoes

Schichtel, Peter and Englert, Christoph and Gerwick, Erik and Plehn, Tilman and Schumann, Steffen. From Jet Counting to Jet Vetoes. 20th International Workshop on Deep-Inelastic Scattering and Related Subjects. 2012. doi:10.3204/DESY-PROC-2012-02/230. arXiv:1206.0544

2012 arXiv
[278]

Supersymmetry production cross sections in pp collisions at s =7 TeV

Kramer, Michael and Kulesza, Anna and van der Leeuw, Robin and Mangano, Michelangelo and Padhi, Sanjay and Plehn, Tilman and Portell, Xavier. Supersymmetry production cross sections in pp collisions at s =7 TeV. 2012. arXiv:1206.2892

2012 arXiv
[279]

Stop searches in 2012

Plehn, Tilman and Spannowsky, Michael and Takeuchi, Michihisa. Stop searches in 2012. JHEP. 2012. doi:10.1007/JHEP08(2012)091. arXiv:1205.2696

2012 arXiv
[280]

Measuring Higgs Couplings from LHC Data

Klute, Markus and Lafaye, Remi and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk. Measuring Higgs Couplings from LHC Data. Phys. Rev. Lett. 2012. doi:10.1103/PhysRevLett.109.101801. arXiv:1205.2699

2012 arXiv
[281]

Sgluon Pair Production to Next-to-Leading Order

Goncalves-Netto, Dorival and Lopez-Val, David and Mawatari, Kentarou and Plehn, Tilman and Wigmore, Ioan. Sgluon Pair Production to Next-to-Leading Order. Phys. Rev. D. 2012. doi:10.1103/PhysRevD.85.114024. arXiv:1203.6358

2012 arXiv
[282]

and others

Altheimer, A. and others. Jet Substructure at the Tevatron and LHC: New Results, New Tools, New Benchmarks. J. Phys. G. 2012. doi:10.1088/0954-3899/39/6/063001. arXiv:1201.0008

2012 arXiv
[283]

MadGolem: automated NLO predictions for Beyond-the-Standard Model searches

Lopez-Val, David and Goncalves-Netto, Dorival and Plehn, Tilman and Wigmore, Ioan and Mawatari, Kentarou. MadGolem: automated NLO predictions for Beyond-the-Standard Model searches. PoS. 2012. doi:10.22323/1.156.0025

2012 doi
[284]

LHC: Standard Higgs and Hidden Higgs

Englert, Christoph and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk and Zerwas, Peter M. LHC: Standard Higgs and Hidden Higgs. Phys. Lett. B. 2012. doi:10.1016/j.physletb.2011.12.067. arXiv:1112.3007

2012 arXiv
[285]

Top Tagging

Plehn, Tilman and Spannowsky, Michael. Top Tagging. J. Phys. G. 2012. doi:10.1088/0954-3899/39/8/083001. arXiv:1112.4441

2012 arXiv
[286]

How to Improve Top Tagging

Plehn, Tilman and Spannowsky, Michael and Takeuchi, Michihisa. How to Improve Top Tagging. Phys. Rev. D. 2012. doi:10.1103/PhysRevD.85.034029. arXiv:1111.5034

2012 arXiv
[287]

W+jets, Z+jets, multijets and new physics searches

Englert, Christoph and Gerwick, Erik and Plehn, Tilman and Schichtel, Peter and Schumann, Steffen. W+jets, Z+jets, multijets and new physics searches. PoS. 2011. doi:10.22323/1.134.0238. arXiv:1110.1043

2011 arXiv
[288]

and Plehn, Tilman and Richardson, Peter

Jaeckel, Joerg and Khoze, Valentin V. and Plehn, Tilman and Richardson, Peter. Travels on the squark-gluino mass plane. Phys. Rev. D. 2012. doi:10.1103/PhysRevD.85.015015. arXiv:1109.2072

2012 arXiv
[289]

Automized Squark-Neutralino Production to Next-to-Leading Order

Binoth, Thomas and Goncalves Netto, Dorival and Lopez-Val, David and Mawatari, Kentarou and Plehn, Tilman and Wigmore, Ioan. Automized Squark-Neutralino Production to Next-to-Leading Order. Phys. Rev. D. 2011. doi:10.1103/PhysRevD.84.075005. arXiv:1108.1250

2011 arXiv
[290]

Understanding Jet Scaling and Jet Vetos in Higgs Searches

Gerwick, Erik and Plehn, Tilman and Schumann, Steffen. Understanding Jet Scaling and Jet Vetos in Higgs Searches. Phys. Rev. Lett. 2012. doi:10.1103/PhysRevLett.108.032003. arXiv:1108.3335

2012 arXiv
[291]

Establishing Jet Scaling Patterns with a Photon

Englert, Christoph and Plehn, Tilman and Schichtel, Peter and Schumann, Steffen. Establishing Jet Scaling Patterns with a Photon. JHEP. 2012. doi:10.1007/JHEP02(2012)030. arXiv:1108.5473

2012 arXiv
[292]

Exploring the Higgs portal

Englert, Christoph and Plehn, Tilman and Zerwas, Dirk and Zerwas, Peter M. Exploring the Higgs portal. Phys. Lett. B. 2011. doi:10.1016/j.physletb.2011.08.002. arXiv:1106.3097

2011 arXiv
[293]

W+Jets at CDF: Evidence for Top Quarks

Plehn, Tilman and Takeuchi, Michihisa. W+Jets at CDF: Evidence for Top Quarks. J. Phys. G. 2011. doi:10.1088/0954-3899/38/9/095006. arXiv:1104.4087

2011 arXiv
[294]

Boosted Semileptonic Tops in Stop Decays

Plehn, Tilman and Spannowsky, Michael and Takeuchi, Michihisa. Boosted Semileptonic Tops in Stop Decays. JHEP. 2011. doi:10.1007/JHEP05(2011)135. arXiv:1102.0557

2011 arXiv
[295]

Jets plus Missing Energy with an Autofocus

Englert, Christoph and Plehn, Tilman and Schichtel, Peter and Schumann, Steffen. Jets plus Missing Energy with an Autofocus. Phys. Rev. D. 2011. doi:10.1103/PhysRevD.83.095009. arXiv:1102.4615

2011 arXiv
[296]

Asymptotic safety and Kaluza-Klein gravitons at the LHC

Gerwick, Erik and Litim, Daniel and Plehn, Tilman. Asymptotic safety and Kaluza-Klein gravitons at the LHC. Phys. Rev. D. 2011. doi:10.1103/PhysRevD.83.084048. arXiv:1101.5548

2011 arXiv
[297]

Charged Higgs production with a top in MC@NLO

Plehn, Tilman and Weydert, Carole. Charged Higgs production with a top in MC@NLO. PoS. 2010. doi:10.22323/1.114.0026. arXiv:1012.3761

2010 arXiv
[298]

Measuring Supersymmetry with Heavy Scalars

Turlay, Emmanuel and Lafaye, Remi and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk. Measuring Supersymmetry with Heavy Scalars. J. Phys. G. 2011. doi:10.1088/0954-3899/38/3/035003. arXiv:1011.0759

2011 arXiv
[299]

Measuring Unification

Adam, Claire and Kneur, Jean-Loic and Lafaye, Remi and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk. Measuring Unification. Eur. Phys. J. C. 2011. doi:10.1140/epjc/s10052-010-1520-2. arXiv:1007.2190

2011 arXiv
[300]

Measuring Hidden Higgs and Strongly-Interacting Higgs Scenarios

Bock, Sebastian and Lafaye, Remi and Plehn, Tilman and Rauch, Michael and Zerwas, Dirk and Zerwas, Peter M. Measuring Hidden Higgs and Strongly-Interacting Higgs Scenarios. Phys. Lett. B. 2011. doi:10.1016/j.physletb.2010.09.032. arXiv:1007.2645

2011 arXiv

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.

[1] [1]

The Flavor of UV Physics

Bruggisser, Sebastian and Sch. The Flavor of UV Physics. JHEP. 2021. doi:10.1007/JHEP05(2021)257. arXiv:2101.07273

arXiv 2021

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Renormalisation group evolution effects on global SMEFT analyses

Bartocci, Riccardo and Biek. Renormalisation group evolution effects on global SMEFT analyses. JHEP. 2025. doi:10.1007/JHEP05(2025)203. arXiv:2412.09674

arXiv 2025

[3] [3]

A global analysis of the SMEFT under the minimal MFV assumption

Bartocci, Riccardo and Biek. A global analysis of the SMEFT under the minimal MFV assumption. JHEP. 2024. doi:10.1007/JHEP05(2024)074. arXiv:2311.04963

arXiv 2024

[4] [4]

A global analysis of axion-like particle interactions using SMEFT fits

Biek. A global analysis of axion-like particle interactions using SMEFT fits. JHEP. 2023. doi:10.1007/JHEP09(2023)120. arXiv:2307.10372

arXiv 2023

[5] [5]

and Laplace, S

Hocker, Andreas and Lacker, H. and Laplace, S. and Le Diberder, F. A New approach to a global fit of the CKM matrix. Eur. Phys. J. C. 2001. doi:10.1007/s100520100729. arXiv:hep-ph/0104062

arXiv 2001

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and Madigan, Maeve and Mantani, Luca and Moore, James M

Costantini, Mark N. and Madigan, Maeve and Mantani, Luca and Moore, James M. A critical study of the Monte Carlo replica method. JHEP. 2024. doi:10.1007/JHEP12(2024)064. arXiv:2404.10056

arXiv 2024

[7] [7]

and others

Dittmaier, S. and others. Handbook of LHC Higgs Cross Sections: 2. Differential Distributions. 2012. doi:10.5170/CERN-2012-002. arXiv:1201.3084

arXiv 2012

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and others

Dittmaier, S. and others. Handbook of LHC Higgs Cross Sections: 1. Inclusive Observables. 2011. doi:10.5170/CERN-2011-002. arXiv:1101.0593

arXiv 2011