Papers › ABCNet: An attention-based method for particle tagging

ABCNet: An attention-based method for particle tagging

13 Jan 2020arXiv:2001.05311links table onlyarchive 2025-07-28

Vinicius Mikuni, Florencia Canelli

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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.

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