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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