Entanglement and Bell Nonlocality in $τ^+ τ^-$ at the LHC using Machine Learning for Neutrino Reconstruction
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914506825793536 |
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| author | Zhang, Yulei Zhou, Bai-Hong Liu, Qi-Bin Wu, Tong Arthur Li, Shu Han, Tao Hsu, Shih-Chieh Low, Matthew |
| author_facet | Zhang, Yulei Zhou, Bai-Hong Liu, Qi-Bin Wu, Tong Arthur Li, Shu Han, Tao Hsu, Shih-Chieh Low, Matthew |
| contents | 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 $pp \to τ^+τ^- X$ to perform quantum tomography and to measure the quantum entanglement and the Bell nonlocality of the $τ^+τ^-$ 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$σ$, establishing $τ^+ τ^-$ 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 $τ^+ τ^-$ 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_01496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Entanglement and Bell Nonlocality in $τ^+ τ^-$ at the LHC using Machine Learning for Neutrino Reconstruction Zhang, Yulei Zhou, Bai-Hong Liu, Qi-Bin Wu, Tong Arthur Li, Shu Han, Tao Hsu, Shih-Chieh Low, Matthew High Energy Physics - Phenomenology High Energy Physics - Experiment Quantum Physics 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 $pp \to τ^+τ^- X$ to perform quantum tomography and to measure the quantum entanglement and the Bell nonlocality of the $τ^+τ^-$ 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$σ$, establishing $τ^+ τ^-$ 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 $τ^+ τ^-$ 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. |
| title | Entanglement and Bell Nonlocality in $τ^+ τ^-$ at the LHC using Machine Learning for Neutrino Reconstruction |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Quantum Physics |
| url | https://arxiv.org/abs/2504.01496 |