Ponente
Descripción
We present the reconstruction and identification of the main decay modes of the lepton $\tau$ in the framework of the Future Circular Collider electron-positron (FCC-ee), using the CLD detector, being one of the first FCC-ee studies based on a realistic (full simulation) detector simulation. Using simulated data from the $e^+e^- \rightarrow Z \rightarrow \tau^+\tau^-$ process, different reconstruction methodologies have been evaluated, comparing classical strategies with machine learning techniques. Specifically, the reconstruction of the $\tau$ lepton -a complex process- has been used to examine in detail the performance of different Particle Flow strategies, comparing established versions -such as the well-known PandoraPFA- with state-of-the-art developments like MLPF. In addition, $\tau$ decay mode identification has been studied for its main channels ($\pi^\pm$, $\rho$, $a_1$), comparing classical strategies (based on the identification of PF-reconstructed candidates such as tracks and photons) with the output of a dedicated neural network, MLID, trained directly on detector signals to infer the decay mode without going through PF. The results show competitive performance in both particle reconstruction and decay mode identification across the different methodologies, reinforcing the potential of these techniques to improve electroweak precision measurements at the FCC-ee and motivating further steps in their development and adaptation. This work provides a foundation for future precision studies of key electroweak observables in the FCC-ee physics program, such as the asymmetries $\mathcal{A}_e$ or $\mathcal{A}_\tau$. For these studies, excellent measurement of $\tau$ lepton properties is essential, including both its energy and position, as well as the characterization of its decay mode.
Abstract
We present the reconstruction and identification of the main decay modes of the lepton $\tau$ in the framework of the Future Circular Collider electron-positron (FCC-ee), using the CLD detector, being one of the first FCC-ee studies based on a realistic (full simulation) detector simulation. Using simulated data from the $e^+e^- \rightarrow Z \rightarrow \tau^+\tau^-$ process, different reconstruction methodologies have been evaluated, comparing classical strategies with machine learning techniques. Specifically, the reconstruction of the $\tau$ lepton -a complex process- has been used to examine in detail the performance of different Particle Flow strategies, comparing established versions -such as the well-known PandoraPFA- with state-of-the-art developments like MLPF. In addition, $\tau$ decay mode identification has been studied for its main channels ($\pi^\pm$, $\rho$, $a_1$), comparing classical strategies (based on the identification of PF-reconstructed candidates such as tracks and photons) with the output of a dedicated neural network, MLID, trained directly on detector signals to infer the decay mode without going through PF. The results show competitive performance in both particle reconstruction and decay mode identification across the different methodologies, reinforcing the potential of these techniques to improve electroweak precision measurements at the FCC-ee and motivating further steps in their development and adaptation. This work provides a foundation for future precision studies of key electroweak observables in the FCC-ee physics program, such as the asymmetries $\mathcal{A}_e$ or $\mathcal{A}_\tau$. For these studies, excellent measurement of $\tau$ lepton properties is essential, including both its energy and position, as well as the characterization of its decay mode.
