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.
Highlights
To expand on this concept, we propose a graph neural network enhanced by attention mechanisms called attention-based cloud net (ABCNet)
The attention-based cloud net (ABCNet) takes advantage of the data structure commonly found in particle colliders to create a point cloud interpretation
ABCNet can be used for event-by-event classification problems or generalised to particle-by-particle classification
Summary
To show the performance and flexibility of the model, two critical problems are investigated: quark–gluon discrimination and pileup mitigation
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