  At the Large Hadron Collider, the high transverse-momentum events studied by
experimental collaborations occur in coincidence with parasitic low
transverse-momentum collisions, usually referred to as pileup. Pileup
mitigation is a key ingredient of the online and offline event reconstruction
as pileup affects the reconstruction accuracy of many physics observables. We
present a classifier based on Graph Neural Networks, trained to retain
particles coming from high-transverse-momentum collisions, while rejecting
those coming from pileup collisions. This model is designed as a refinement of
the PUPPI algorithm, employed in many LHC data analyses since 2015. Thanks to
an extended basis of input information and the learning capabilities of the
considered network architecture, we show an improvement in pileup-rejection
performances with respect to state-of-the-art solutions.
