Speaker
Huilin Qu
(Univ. of California Santa Barbara (US))
Description
How to represent a jet is one of the key aspects of machine learning algorithms for jet physics. Motivated by recent progress in machine learning community on point cloud recognition, we propose a new approach that represents a jet as an unordered set of particles with their measured properties, effectively a "particle" cloud. Specialized algorithms for point cloud recognition, e.g., Dynamic Graph CNN, are explored, and the performance on jet classification is compared to alternative approaches using state-of-the-art 2D CNN models on jet images and 1D CNN models on jet constituent particles.
Primary authors
Huilin Qu
(Univ. of California Santa Barbara (US))
Loukas Gouskos
(Univ. of California Santa Barbara (US))