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arxiv 2109.10414 v3 pith:KV2NXCWG submitted 2021-09-21 hep-ph

Anja Butter , Tilman Plehn , Nathalie Soybelman , Johann Brehmer This is my paper
classification hep-ph
keywords productionactualassociatedattractivebackcasecoefficientcp-violation
verification ladder T0 review T1 audit T2 compute T3 formal
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While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We show how symbolic regression trained on matrix-element information provides, for instance, optimal LHC observables in an easily interpretable form. We introduce the method using the effect of a dimension-6 coefficient on associated ZH production. We then validate it for the known case of CP-violation in weak-boson-fusion Higgs production, including detector effects.

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Forward citations

Cited by 4 Pith papers

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

  1. Neural Control Variates at LO and NLO

    hep-ph 2026-07 accept novelty 7.0 of 10

    Signed neural control variates from normalizing flows, combined with neural importance sampling, reduce weight ranges and negative weights for LO and NLO phase-space integration and event generation.

  2. Agentic Re-Casting using Agentic Re-Simulations

    hep-ph 2026-07 conditional novelty 6.0 of 10

    An agentic AI system with a physicist in the loop re-casts an ATLAS ttZ measurement into a global top-quark SMEFT fit and recovers injected coloron Wilson coefficients in a repeatable benchmark.

  3. $\mathcal{CP}$-Analyses with Symbolic Regression

    hep-ph 2025-07 conditional novelty 6.0 of 10

    Symbolic regression produces analytic, detector-level CP-odd observables for WBF Higgs production and an analytic reconstruction of the Collins-Soper angle in ttH that are competitive with black-box ML and classical methods.

  4. Toward Supporting Narrative-Driven Data Exploration: Barriers and Design Opportunities

    cs.HC 2025-08 reject novelty 4.0 of 10

    Clustering particles by mass, spin, lifetime and decay modes with conventional tools reproduces known Standard Model groupings, but the dataset and algorithm choices quietly encode the theory being 'rediscovered'.

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