Experiments at the High-Luminosity LHC and the Future Circular Collider need efficient algorithms to reconstruct granular events expected at such detectors with high fidelity. 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. Accurate reconstruction can significantly improve future measurements at colliders. The resulting model is portable across Nvidia, AMD and Habana hardware. Our datasets and software are published following the findable, accessible, interoperable, and reusable principles.

翻译：高亮度LHC与未来环形对撞机（FCC）的实验需要高效算法，以高保真度重建此类探测器预期产生的颗粒化事件。我们基于全探测器模拟，研究了适用于正负电子对撞事件重建的可扩展机器学习模型。粒子流重建可形式化为利用径迹与量热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，证明可在避免二次运算的同时实现逼真的重建效果。研究表明，超参数调优显著提升了模型性能。最优图神经网络模型在喷注横动量分辨率上较基于规则的算法提升高达50%。高精度重建将显著改进对撞机上未来测量的准确性。所得模型可跨Nvidia、AMD及Habana硬件平台移植。我们的数据集与软件遵循可发现、可访问、可互操作、可复用（FAIR）原则公开发布。