ParticleTransformer is all you need for reconstructing hadronic tau leptons
ParticleTransformer 是重建强子性 tau 轻子所需的一切
Nalong-Norman Seeba, Laurits Tani, Torben Lange, Joosep Pata
AI总结 针对 FCC-ee 的 TeraZ 计划,提出首个全机器学习强子 tau 重建方法,通过多任务模型实现识别、衰变模式分类、电荷重建和四动量回归,性能优于传统方法。
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在 FCC-ee 的 TeraZ 计划期间,预计将产生大量 $Z \ ightarrow \ au\ au$ 事件,这将允许进行精确测量和寻找超越标准模型的新物理,因此需要准确重建强子性衰变的 tau 轻子。由于存在未被探测到的中微子以及强子 tau 衰变的多样化拓扑结构,这种重建尤其具有挑战性,使得设计稳健的启发式重建算法变得困难。在这项工作中,我们提出了首个完全机器学习的强子 tau 重建方法,专为 FCC-ee 研究而调整。重建被表述为一组互补任务,包括 tau 识别、衰变模式分类、电荷重建和完整四动量回归。算法在使用 CLD 探测器设置、具有真实探测器效应的全模拟电子-正电子对撞样本上进行评估。我们将专用的任务特定模型与统一的多任务模型进行比较,并在所有重建任务中以细粒度方式量化其性能。两种方法在高信号效率下均达到千分位水平的 tau 误识别率,主要通道的衰变模式分类 F1 分数高达 0.95,以及亚千分位水平的电荷误识别率,比传统的喷注电荷估计器性能高出两个数量级。对于完整运动学重建,模型实现了千分位水平的角分辨率和百分位水平的可见横向动量分辨率,超过了重建级喷注观测量。所得模型为 FCC-ee 的强子 tau 重建提供了现实的高性能解决方案,包括识别、电荷区分、衰变模式分析和完整运动学重建。
The large number of $Z \rightarrow ττ$ events expected during the TeraZ program at FCC-ee will allow for precision measurements and searches for physics beyond the Standard Model, requiring accurate reconstruction of hadronically decaying tau leptons. This reconstruction is particularly challenging due to the presence of undetected neutrinos and the diverse topology of hadronic tau decays, making the design of robust heuristic reconstruction algorithms challenging. In this work, we present the first fully machine learned hadronic tau reconstruction approach tuned for FCC-ee studies. The reconstruction is formulated as a set of complementary tasks, including tau identification, decay mode classification, charge reconstruction, and full four-momentum regression. The algorithms are evaluated on fully simulated electron--positron collision samples with realistic detector effects using the CLD detector setup. We compare dedicated task-specific models with a unified multi-task model and quantify their performance in a granular manner across all reconstruction tasks. Both approaches achieve per-mille-level tau mis-identification rates at high signal efficiency, decay mode classification F1 scores of up to 0.95 for the dominant channels, and sub-per-mille charge mis-identification rates, outperforming a conventional jet-charge estimator by up to two orders of magnitude. For the full kinematic reconstruction, the models achieve per-mille-level angular resolution and percent-level visible transverse momentum resolution, exceeding the performance of reconstruction-level jet observables. The resulting models provide a realistic high-performance solution for hadronic tau reconstruction at FCC-ee, offering identification, charge discrimination, decay mode analysis and full kinematic reconstruction.