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arXiv · 2001.05311

ABCNet: An attention-based method for particle tagging

Vinicius Mikuni·Florencia Canelli
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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.

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Vinicius Mikuni, Florencia Canelli. 2020-01-13. ABCNet: An attention-based method for particle tagging. https://doi.org/10.1140/epjp/s13360-020-00497-3

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