Point cloud transformers applied to collider physics

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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@article{Mikuni_2021,
doi = {10.1088/2632-2153/ac07f6},
url = {https://dx.doi.org/10.1088/2632-2153/ac07f6},
year = {2021},
month = {jul},
publisher = {IOP Publishing},
volume = {2},
number = {3},
pages = {035027},
author = {Vinicius Mikuni and Florencia Canelli},
title = {Point cloud transformers applied to collider physics},
journal = {Machine Learning: Science and Technology},
}