Sensitivity study of the tau lepton electric dipole moment at the Super Tau-Charm Facility
This study investigates the intrinsic electric dipole moment (EDM) of the τ lepton, which is an important quantity in the search for physics beyond the Standard Model (BSM). In preparation for future measurements at the Super Tau-Charm Facility (STCF), we employ Monte Carlo simulations of the proces...
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Published in | Chinese physics C Vol. 49; no. 11; p. 113001 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
01.11.2025
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Online Access | Get full text |
ISSN | 1674-1137 2058-6132 2058-6132 |
DOI | 10.1088/1674-1137/adf6e0 |
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Abstract | This study investigates the intrinsic electric dipole moment (EDM) of the τ lepton, which is an important quantity in the search for physics beyond the Standard Model (BSM). In preparation for future measurements at the Super Tau-Charm Facility (STCF), we employ Monte Carlo simulations of the process and optimize the analysis methodology for EDM extraction. Machine learning techniques are implemented to efficiently identify signal events ( ), which result in a significant improvement in signal-to-noise ratio. Our optimized event selection algorithm achieves 80.0% signal purity with 6.3% efficiency. We develop an analytical approach for τ lepton momentum reconstruction and derive the squared spin density matrix along with optimal observables, which maximize the sensitivity to . The relationship between these observables and the EDM is established with the estimated sensitivity of at a 68% confidence level. These results provide a foundation for future experimental measurements of the τ lepton EDM in STCF experiments. |
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AbstractList | This study investigates the intrinsic electric dipole moment (EDM) of the τ lepton, which is an important quantity in the search for physics beyond the Standard Model (BSM). In preparation for future measurements at the Super Tau-Charm Facility (STCF), we employ Monte Carlo simulations of the process and optimize the analysis methodology for EDM extraction. Machine learning techniques are implemented to efficiently identify signal events ( ), which result in a significant improvement in signal-to-noise ratio. Our optimized event selection algorithm achieves 80.0% signal purity with 6.3% efficiency. We develop an analytical approach for τ lepton momentum reconstruction and derive the squared spin density matrix along with optimal observables, which maximize the sensitivity to . The relationship between these observables and the EDM is established with the estimated sensitivity of at a 68% confidence level. These results provide a foundation for future experimental measurements of the τ lepton EDM in STCF experiments. |
Author | Wu 吴, Yongcheng 永成 Zhou 周, Xiaorong 小蓉 Sun 孙, Xulei 旭磊 |
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