Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

arXiv:2309.06782v5 Announce Type: replace-cross
Abstract: Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. 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 …

abstract algorithms arxiv cs.lg current event flow future hep-ex machine machine learning machine learning models networks neural networks particle physics.data-an physics.ins-det scalable stat.ml study type