ABCNet: an attention-based method for particle tagging
V. M. Mikuni, F. Canelli
Abstract
In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we propose a graph neural network enhanced by attention mechanisms called ABCNet. To exemplify the advantages and flexibility of treating collider data as a point cloud, two physically motivated problems are investigated: quark-gluon discrimination and pileup reduction. The former is an event-by-event classification, while the latter requires each reconstructed particle to receive a classification score. For both tasks, ABCNet shows an improved performance compared to other algorithms available.
Topics & Concepts
Computer scienceImplementationFlexibility (engineering)Particle physicsGraphEvent (particle physics)Artificial intelligenceMachine learningTheoretical computer sciencePhysicsProgramming languageMathematicsQuantum mechanicsStatisticsParticle physics theoretical and experimental studiesMedical Imaging Techniques and ApplicationsDistributed and Parallel Computing Systems