Machine learning the Higgs boson-top quark CP phase

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.

Published version in PRD, 12 pages, 5 figures

References in corpus (11)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• Higgs characterisation at NLO in QCD: CP properties of the top-quark Yukawa interaction
• Spin Correlation Effects in Top Quark Pair Production at the LHC
• Determining the CP parity of Higgs bosons at the LHC in their tau decay channels
• Constraining CP-violating Higgs Sectors at the LHC using gluon fusion
• Recursive Jigsaw Reconstruction: HEP event analysis in the presence of kinematic and combinatoric ambiguities
• Implications of CP-violating Top-Higgs Couplings at LHC and Higgs Factories
• Probing the CP structure of the top quark Yukawa coupling: Loop sensitivity vs. on-shell sensitivity
• Boosting to identify: pseudoscalar searches with di-leptonic tops
• Approaching robust EFT limits for CP-violation in the Higgs sector
• $t\bar{t}b\bar{b}$ at the LHC: On the size of corrections and $b$-jet definitions

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• Probing the CP Structure of the Top Quark Yukawa at the Future Muon Collider
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• Advancing Tools for Simulation-Based Inference
• Hadronic Top Quark Polarimetry with ParticleNet
• Determining the $CP$ Property of $ht\bar{t}$ Coupling via a Novel Jet Substructure Observable
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• Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties
• Top-philic Machine Learning
• BitHEP -- The Limits of Low-Precision ML in HEP
• Sensitivity to $\mathcal{C}\mathcal{P}$-violating effective couplings in the top-Higgs sector
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