Future collider experiments require unprecedented precision in measurements of Higgs, electroweak, and flavour observables, placing stringent demands on event reconstruction. The achievable precision on Higgs couplings scales directly with the resolution on visible final state particles and their invariant masses. Current particle flow algorithms rely on detector specific clustering, limiting flexibility during detector design. Here we present an end-to-end global event reconstruction approach that maps charged particle tracks and calorimeter and muon hits directly to particle level objects. The method combines geometric algebra transformer networks with object condensation based clustering, followed by dedicated networks for particle identification and energy regression. Our approach is benchmarked on fully simulated electron positron collisions at FCC-ee using the CLD detector concept. It outperforms the state-of-the-art rule-based algorithm by 10--20\% in relative reconstruction efficiency, achieves up to two orders of magnitude reduction in fake-particle rates for charged hadrons, and improves visible energy and invariant mass resolution by 22\%. By decoupling reconstruction performance from detector-specific tuning, this framework enables rapid iteration during the detector design phase of future collider experiments.

翻译：未来对撞机实验对希格斯粒子、电弱相互作用以及味物理观测量提出了前所未有的测量精度要求，这对事件重建技术提出了严格挑战。希格斯耦合常数的可达到精度直接取决于可见未态粒子及其不变质量的分辨率。现有的粒子流算法依赖于探测器特定的聚类方法，限制了探测器设计阶段的灵活性。本文提出一种端到端的全局事件重建方法，将带电粒子径迹、量能器与μ子探测器击中直接映射至粒子层级对象。该方法结合了几何代数Transformer网络与基于对象凝聚的聚类技术，并辅以专门的粒子鉴别与能量回归网络。我们在采用CLD探测器概念的FCC-ee正负电子对撞完全模拟数据上对该方法进行基准测试。结果显示：相对重建效率较当前最先进的基于规则的算法提升10–20%；带电强子的赝粒子产生率降低达两个数量级；可见能量与不变质量分辨率提升22%。通过将重建性能与探测器特定调参解耦，该框架为未来对撞机实验的探测器设计阶段提供了快速迭代能力。