Machine Learning for Particle Flow Reconstruction at CMS

Understand

We provide details on the implementation of a machine-learning based particle flow algorithm for CMS.

• The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy.
• We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network.
• The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event.