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Machine learning the Higgs boson-top quark CP phase

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arxiv 2110.07635 v2 pith:WEBEIKYK submitted 2021-10-14 hep-ph hep-ex

classification hep-phhep-ex
keywords combinationgammahiggs-toplearninglesssimmachinephasepolarization
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We explore the direct Higgs-top CP measurement via the $pp\to t\bar{t}h$ channel at the high-luminosity LHC. We show that a combination of machine learning techniques and efficient kinematic reconstruction methods can boost new physics sensitivity, effectively probing the complex $t\bar{t}h$ multi-particle phase space. Special attention is devoted to top quark polarization observables, uplifting the analysis from a raw rate to a polarization study. Through a combination of hadronic, semi-leptonic, and di-leptonic top pair final states in association with $h\to \gamma\gamma$, we obtain that the HL-LHC can probe the Higgs-top coupling modifier and CP-phase, respectively, up to $|\kappa_t|\lesssim 8\%$ and $|\alpha|\lesssim 13^{\circ}$ at $68\%$~CL.

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Cited by 4 Pith papers

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

  1. Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties

    hep-ph 2025-08 conditional novelty 6.0 of 10

    SAGE, a dual-branch GNN trained under nuisance fluctuations, estimates the Higgs signal strength with near-nominal coverage (0.662-0.683) but wider intervals than the top FAIR-HUC leaderboard methods.

  2. Simulation-Prior Independent Neural Unfolding Procedure

    hep-ph 2025-07 conditional novelty 6.0 of 10

    SPINUP is a neural-unfolding method that fits a parton-level generative model directly to detector-level data through a learned forward simulator, aiming to remove the simulation-prior bias.

  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. Unbinning global LHC analyses

    hep-ph 2025-09 conditional novelty 5.0 of 10

    Simulation-based inference produces stronger combined LHC constraints on SMEFT Wilson coefficients than histogram-based inference for four di-boson processes.

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