Pith. sign in

REVIEW 3 cited by

Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2110.10177 v2 pith:MXIFQ6UB submitted 2021-10-19 hep-ph

classification hep-ph
keywords higgsboundscouplingtop-yukawaconstrainedcp-violatingexclusionexpected
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

While CP violation in the Higgs interactions with massive vector boson is already tightly constrained, the CP nature of the Higgs interactions with fermions is far less constrained. In this work, we assess the potential of machine-learning-based inference methods to constrain CP violation in the Higgs top-Yukawa coupling. This approach enables the use of the full available kinematic information. Concentrating on top-associated Higgs production with the Higgs decaying to two photons, we derive expected exclusion bounds for the LHC and the high-luminosity phase of the LHC. We also study the dependence of these bounds on the Higgs interaction with massive vector bosons and their robustness against theoretical uncertainties. In addition to deriving expected exclusion bounds, we discuss at which level a non-zero CP-violating top-Yukawa coupling can be distinguished from the SM. Moreover, we analyze which kinematic distributions are most sensitive to a CP-violating top-Yukawa coupling.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 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. 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.

Pith tools