发表机构
Toshiko Yuasa Laboratory (TYL), Tsukuba; Centre de Physique des Particules de Marseille (CPPM), Marseille（Tsukuba 八坂敏子实验室; 马赛粒子物理中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍 Belle II 全事件解释算法，通过分层重建和多变量分类标记 B 介子，改进训练与校准，提升重建性能与背景抑制。
AI 中文摘要
全事件解释（FEI）算法是 Belle II 分析框架的核心组成部分，旨在高效重建独有 B 介子衰变。FEI 对强子型和半轻子型终态进行分层重建，利用多变量分类技术来标记正负电子对撞中产生的两个 B 介子之一。FEI 对于涉及缺失能量的信号侧衰变研究尤为重要。通过 FEI 重建一个 B 介子，另一个信号侧 B 介子的运动学被严格约束，从而改善了背景抑制。在本文中，我们介绍了 FEI 的设计、实现和最新进展。我们概述了重建策略、候选选择以及基于梯度提升决策树的多阶段多变量训练。详细讨论了 FEI 工作流程的近期改进，包括用于训练模拟数据的改进、中间粒子重建的改进以及多变量输入鲁棒性的增强。除算法更新外，我们还讨论了新开发的 FEI 校准和性能评估技术。这些技术包括改进的效率校准程序、跨衰变通道和数据采集条件下的性能检查，以及应用重加权技术来诊断和缓解模拟与数据之间的差异。最后，我们简要讨论了旨在进一步提高重建性能和计算效率的正在进行和未来的发展。
英文摘要
The Full Event Interpretation (FEI) algorithm is a central component of the Belle II analysis framework, designed for the efficient reconstruction of exclusive B-meson decays. The FEI performs a hierarchical reconstruction of hadronic and semileptonic final states, using multivariate classification techniques to tag one of the two B mesons produced in electron-positron collisions. The FEI is particularly important for studies of signal-side decays involving missing energy. By reconstructing one B meson with the FEI, the kinematics of the other, signal-side B meson are tightly constrained, leading to improved background rejection. In this contribution, we present the design, implementation, and recent developments of the FEI. We outline the reconstruction strategy, candidate selection, and multistage multivariate training based on gradient boosted decision trees. Recent improvements to the FEI workflow, including improvements in the simulated data used for training, reconstruction of intermediate particles, and improved robustness of the multivariate inputs, are discussed in detail. Beyond algorithmic updates, we also discuss newly developed techniques for the calibration and performance evaluation of the FEI. These include refined procedures to calibrate the efficiency, check the performance across decay channels and data-taking conditions, and application of reweighting techniques to diagnose and mitigate discrepancies between simulation and data. Finally, we briefly discuss ongoing and future developments aimed at further improving reconstruction performance and computational efficiency.
