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Probing the CP structure of the top quark Yukawa coupling: Loop sensitivity vs. on-shell sensitivity

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arxiv 2104.04277 v2 pith:3X52NU52 submitted 2021-04-09 hep-ph hep-ex

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

The question whether the Higgs boson is connected to additional CP violation is one of the driving forces behind precision studies at the Large Hadron Collider. In this work, we investigate the CP structure of the top quark Yukawa interaction-one of the most prominent places for searching for New Physics-through Higgs boson loops in top quark pair production. We calculate the electroweak corrections including arbitrary CP mixtures at next-to-leading-order in the Standard Model Effective Field Theory. This approach of probing Higgs boson degrees of freedom relies on the large $t\bar{t}$ cross section and the excellent perturbative control. In addition, we consider all direct probes with on-shell Higgs boson production in association with a single top quark or top quark pair. This allows us to contrast loop sensitivity versus on-shell sensitivity in these fundamentally different process dynamics. We find that loop sensitivity in $t\bar{t}$ production and on-shell sensitivity in $t\bar{t}H$ and $tH$ provide complementary handles over a wide range of parameter space.

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Cited by 2 Pith papers

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

  1. Quantum Tomography of Top Quarks as a Probe of Charge-Parity Violation

    hep-ph 2026-07 conditional novelty 7.0 of 10

    Combining the full 15-parameter spin-state (quantum tomography) data on top-quark pairs with a complete one-loop calculation bounds the CP-violating part of the top-Higgs coupling at a level comparable to dedicated tt...

  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.

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