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 inChinese physics C Vol. 49; no. 11; p. 113001
Main Authors Sun 孙, Xulei 旭磊, Wu 吴, Yongcheng 永成, Zhou 周, Xiaorong 小蓉
Format Journal Article
LanguageEnglish
Published 01.11.2025
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ISSN1674-1137
2058-6132
2058-6132
DOI10.1088/1674-1137/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.
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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