Point Cloud Transformers applied to Collider Physics
This work addresses the problem of efficiently processing point cloud data in collider physics for physicists, offering an incremental application of an existing method.
This paper applies a modified Transformer network, the Point Cloud Transformer, to analyze unordered sets of particles from collider events. The method is evaluated on jet-tagging applications for highly-boosted particles.
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.