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Entanglement and Bell Nonlocality in τ+τ- at the LHC using Machine Learning for Neutrino Reconstruction

Not scheduled
20m

Speaker

Baihong Zhou (TDLI, SJTU)

Description

Experiments at the CERN Large Hadron Collider (LHC) have accumulated an unprecedented amount of data corresponding to a large variety of quantum states. Although searching for new particles beyond the Standard Model of particle physics remains a high priority for the LHC program, precision measurements of the physical processes predicted in the Standard Model continue to lead us to a deeper understanding of nature at high energies. We carry out detailed simulations for the process $pp \rightarrow \tau^+ \tau^-$ to perform quantum tomography and to measure the quantum entanglement and the Bell nonlocality of the $\tau^+ \tau^-$ two-qubit state, including both statistical and systematic uncertainties. By using advanced machine learning techniques for neutrino momentum reconstruction, we achieve precise measurements of the full spin density matrix, a critical advantage over previous studies limited by reconstruction challenges for missing momenta. Our analysis reveals a clear observation of Bell nonlocality with high statistical significance, surpassing 5$\sigma$, establishing $\tau^+ \tau^-$ as an ideal system for quantum information studies in high-energy collisions. Given its experimental feasibility and the high expected sensitivity for Bell nonlocality, we propose that $\tau^+ \tau^-$ should be regarded as the new benchmark system for quantum information studies at the LHC, complementing and extending the insights gained from the $ t\bar{t}$ system.

Primary authors

Baihong Zhou (TDLI, SJTU) Matthew Low (University of Pittsburgh) Qibin LIU (SLAC National Accelerator Laboratory) Shih-Chieh Hsu (University of Washington) Shu Li (TDLI, SJTU) Tao Han (University of Pittsburgh) Tong Arthur Wu (University of Pittsburgh) Yulei Zhang (University of Washington)

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