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 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 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. 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 principles.

翻译：我们研究了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。粒子流重建可表述为一项监督学习任务，利用径迹与量热器簇或击中信息。我们比较了图神经网络与基于核的Transformer，证明两者均避免了二次内存分配和计算成本，同时实现了真实感重建。研究表明，在超级计算机上进行超参数调优显著提升了模型的物理性能，与基准相比，喷注横向动量分辨率提升高达50%。所得模型在各类硬件处理器上具有高度可移植性。最后，我们证明该模型可在包含径迹与量热器击中的高粒度输入上进行训练，并获得与基准相当的物理性能。为复现本研究的数据集和软件遵循可查找、可访问、可互操作、可重用原则发布。