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Optimized probes of $CP$-odd effects in the $t \bar{t} h$ process at hadron colliders
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abstract
We use machine learning (ML) and non-ML techniques to study optimized $CP$-odd observables, directly and maximally sensitive to the $CP$-odd $i \tilde \kappa \bar t \gamma^5 t h$ interaction at the LHC and prospective future hadron colliders using the final state with a Higgs boson and a top quark pair, $pp\to t\bar t h$, followed by semileptonic $t$ decays. We perform phase-space optimization of manifestly $CP$-odd observables ($\boldsymbol \omega$), sensitive to the sign of $\tilde \kappa$, and constructed from experimentally accessible final state momenta. We identify a simple optimized linear combination $\boldsymbol \alpha\cdot \boldsymbol\omega$ that gives similar sensitivity as the studied fully fledged ML models. Using $\boldsymbol\alpha\cdot \boldsymbol\omega$ we project the expected sensitivities to $\tilde \kappa$ at HL-LHC, HE-LHC, and FCC-hh.
Forward citations
Cited by 2 Pith papers
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Simulation-Prior Independent Neural Unfolding Procedure
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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$\mathcal{CP}$-Analyses with Symbolic Regression
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.
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