Speaker
Nathaniel Sherlock Woodward
(Massachusetts Inst. of Technology (US))
Description
Particle jets exhibit tree-like structures through stochastic showering and hadronization. The hierarchical nature of these structures aligns naturally with hyperbolic space, a non-Euclidean geometry that captures hierarchy intrinsically. Drawing upon the foundations of geometric learning, we introduce hyperbolic transformer models tailored for tasks relevant to jet analyses, such as classification and representation learning. Through jet embeddings and jet tagging evaluations, our hyperbolic approach outperforms its Euclidean counterparts. These findings underscore the potential of using hyperbolic geometric representations in advancing jet physics analyses.
Author
Nathaniel Sherlock Woodward
(Massachusetts Inst. of Technology (US))
Co-authors
Jeffrey Krupa
(Massachusetts Institute of Technology)
Philip Coleman Harris
(Massachusetts Inst. of Technology (US))
