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Point Cloud Transformers applied to Collider Physics
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Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified Transformer network called Point Cloud Transformer as a method to incorporate the advantages of the Transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.
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Cited by 3 Pith papers
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A BART transformer can generate gauge-invariant Lagrangians from field content with over 90% accuracy on in-distribution data, though its performance drops on realistic Standard Model benchmarks.
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E-PCN reaches 94.67% macro-accuracy on 10-class jet tagging by weighting graphs with angular separation, transverse momentum, momentum fraction, and invariant mass, with Grad-CAM showing the first two account for 76% ...
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