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 enhances the physics performance of the models, improving the jet transverse momentum resolution by up to 50% compared to the baseline. 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.

翻译：我们研究基于高粒度探测器模拟的高能正负电子对撞中全事件重建的可扩展机器学习模型。粒子流重建可被形式化为利用径迹与量能器簇团或击中点的有监督学习任务。我们对比了图神经网络与基于核函数的Transformer模型，证明两者均能避免二次内存分配与计算开销，同时实现实际的粒子流重建效果。研究显示，在超级计算机上进行的超参数调优可显著提升模型的物理性能，相较于基线模型，喷注横动量分辨率最高提升50%。所得模型具备高度跨硬件处理器可移植性，支持Nvidia、AMD及Intel Habana加速卡。最后，我们证明该模型可基于包含径迹与量能器击中点的高粒度输入进行训练，并取得与基线模型相当的竞争性物理性能。相关数据集与软件代码已遵循可发现、可访问、可互操作、可复用原则公开发布。