Semi-supervised Graph Neural Networks for Pileup Noise Removal

Understand

The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton-proton interactions in the same or nearby bunch crossings (pileup).

• Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables.
• This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup.
• The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing.