Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.

翻译：在高亮度大型强子对撞机及未来环形对撞机预期的高粒度探测器中，需要高效精确的算法来重建粒子。本研究基于完整探测器模拟，探讨了电子-正电子对撞事件重建的可扩展机器学习模型。粒子流重建可被构建为利用径迹与量能器簇的监督学习任务。我们比较了图神经网络与基于核的Transformer模型，证明在实现真实重建的同时能够避免二次运算。研究表明超参数调优能显著提升模型性能。最佳图神经网络模型相较于基于规则的算法，在喷注横向动量分辨率上提升最高达50%。所得模型可跨Nvidia、AMD及Habana硬件平台移植。基于机器学习的精确快速重建将显著提升未来对撞机实验的测量能力。