端到端重建提升未来对撞机物理精度，显著优化粒子分辨与假事例率。
End-to-end event reconstruction for precision physics at future colliders
- 用几何代数变换器+对象凝聚聚类，直接从探测器信号重构粒子级对象。
- 在FCC-ee上比现有算法效率高10%~20%，假带电强子事例减少100倍。
- 提升可见能量和不变质量分辨率22%，适用于未来对撞机快速设计迭代。
未来对撞机实验对希格斯、电弱及味物理测量的精度要求空前严苛，对事件重建提出更高要求。希格斯耦合的可实现精度与可见末态粒子及其不变质量的分辨率直接相关。当前粒子流算法依赖于探测器特定的聚类方式，限制了探测器设计中的灵活性。本文提出一种端到端全局事件重建方法，将带电粒子轨迹、量能器和μ子探测器的触发信号直接映射为粒子级对象。该方法结合几何代数变换器网络与基于对象凝聚的聚类，再通过专用网络进行粒子识别与能量回归。在采用CLD探测器概念的全模拟电子正电子碰撞（FCC-ee）场景下进行基准测试，其相对重建效率较最先进规则算法提升10%~20%，对带电强子的假事例率降低达两个数量级，可见能量与不变质量分辨率提升22%。该框架解耦了重建性能与探测器特性的依赖，支持未来对撞机探测器设计阶段的快速迭代。
原文摘要 · Abstract (English)
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.
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