https://doi.org/10.1140/epjp/s13360-020-00497-3
Regular Article
ABCNet: an attention-based method for particle tagging
University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
Received:
18
February
2020
Accepted:
28
May
2020
Published online:
3
June
2020
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.
© The Author(s) 2020
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