End-to-end event reconstruction for precision physics at future colliders

AI 总结

• 提出从径迹、量能器和缪子击中信息直接映射到粒子级对象的端到端重建方法。
• 方法结合几何代数Transformer与object condensation聚类，并接入粒子鉴别与能量回归网络。
• 在FCC-ee的CLD探测器概念全模拟电子-正电子碰撞上完成基准测试。
• 相较最先进规则式算法，重建相对效率提升约10–20%，带电强子伪粒子率最高降低近两个数量级。
• 可见能量与不变质量分辨率提升约22%，且减少对探测器特定调参的依赖。

内容摘录

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.