We study scalable machine learning models for full event reconstruction in
high-energy electron-positron collisions based on a highly granular detector
simulation. Particle-flow (PF) reconstruction can be formulated as a supervised
learning task using tracks and calorimeter clusters or hits. We compare a graph
neural network and kernel-based transformer and demonstrate that both avoid
quadratic memory allocation and computational cost while achieving realistic PF
reconstruction. We show that hyperparameter tuning on a supercomputer
significantly improves the physics performance of the models. We also
demonstrate that the resulting model is highly portable across hardware
processors, supporting Nvidia, AMD, and Intel Habana cards. Finally, we
demonstrate that the model can be trained on highly granular inputs consisting
of tracks and calorimeter hits, resulting in a competitive physics performance
with the baseline. Datasets and software to reproduce the studies are published
following the findable, accessible, interoperable, and reusable (FAIR)
principles.

研究了可扩展的机器学习模型，用于基于高粒度探测器模拟的高能电子 - 正电子碰撞的完全事件重建。比较了图神经网络和基于核的变压器，并证明两者在实现逼真的粒子流重建时，同时避免了二次内存分配和计算成本。展示了在超级计算机上进行的超参数调整显著改善了模型的物理性能。还展示了所得模型在支持 Nvidia、AMD 和 Intel Habana 卡的硬件处理器上具有高度可移植性。最终证明了该模型可以在由径迹和电量计击中构成的高粒度输入上进行训练，从而获得与基线相竞争的物理性能。根据可寻找、可获取、可互用和可复用（FAIR）原则发布了用于重现研究的数据集和软件。