Pileup mitigation at the Large Hadron Collider with graph neural networks

• Published in: European physical journal plus 2019-07, Vol.134 (7), p.333, Article 333
• Main Authors: Arjona Martínez, J., Cerri, O., Spiropulu, M., Vlimant, J. R., Pierini, M.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied and Technical Physics; Atomic; Charged particles; Collisions; Complex Systems; Condensed Matter Physics; Deep learning; Graph neural networks; Large Hadron Collider; Machine learning; Mathematical and Computational Physics; Molecular; Momentum; Neural networks; Optical and Plasma Physics; Physics; Physics and Astronomy; Reconstruction; Regular Article; Sensors; Theoretical
• ISSN: 2190-5444; 2190-5444
• DOI: 10.1140/epjp/i2019-12710-3

. 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 (D. Bertolini et al. , JHEP 10 , 059 (2014)), 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.