Scalable neural network models and terascale datasets for particle-flow reconstruction
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.