Erlangen 2026 – wissenschaftliches Programm
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T: Fachverband Teilchenphysik
T 25: Higgs Physics III
T 25.6: Vortrag
Dienstag, 17. März 2026, 17:30–17:45, KH 00.016
Quantum tomography using machine learning to infer incomplete information in H → WW* → ℓνℓν — Carsten Burgard1, Vince Croft2, André Sopczak3, •Andrii Vak3, and Lennart Völz1 — 1TU Dortmund University — 22Leiden University — 3CTU in Prague
We present a novel experimental strategy for testing quantum entanglement in Higgs boson decays to W boson pairs at the Large Hadron Collider. Unlike theoretical approaches that rely on expectation values of Bell operators, which are highly sensitive to outliers and detector effects, we introduce a continuous formulation of the CGLMP inequality that enables standard hypothesis testing between entangled and separable states. To overcome the fundamental challenge of reconstructing invisible neutrino momenta in the H → WW* → ℓνℓν channel, we employ conditional denoising diffusion probabilistic models (cDDPM), which provide unbiased, multidimensional unfolding applicable to the full measured dataset including backgrounds. We compare the performance of diffusion-based reconstruction against neural network regression and analytical methods, evaluating each through profile likelihood hypothesis tests implemented in RooFit. Our results demonstrate that the diffusion-based approach enables robust hypothesis testing of quantum entanglement in a realistic collider environment, achieving sensitivity to Bell inequality violation with existing LHC datasets.
Keywords: Higgs boson; quantum entanglement