Point cloud transformers applied to collider physics
TL
TL;DR
A modified transformer network called point cloud transformer is applied 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.
Abstract
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.
References34 items
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PCT: Point cloud transformer
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Zero-permutation jet-parton assignment using a self-attention network
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Graph Neural Networks for Particle Tracking and Reconstruction
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Equivariant energy flow networks for jet tagging
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