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In high energy physics, graph-based implementations have the advantage oftreating the input data sets in a similar way as they are collected by colliderexperiments. To expand on this concept, we propose a graph neural networkenhanced by attention mechanisms called ABCNet. To exemplify the advantages andflexibility of treating collider data as a point cloud, two physicallymotivated problems are investigated: quark-gluon discrimination and pileupreduction. The former is an event-by-event classification while the latterrequires each reconstructed particle to receive a classification score. Forboth tasks ABCNet shows an improved performance compared to other algorithmsavailable.

ABCNet: An attention-based method for particle tagging