Discovering Sparse Representations of Lie Groups with Machine Learning
High Energy Physics - Phenomenology
2023-08-09 v1 Machine Learning
Mathematical Physics
Group Theory
math.MP
Abstract
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter, we extend this technique to derive sparse representations of arbitrary Lie algebras. We show that our method reproduces the canonical (sparse) representations of the generators of the Lorentz group, as well as the and families of Lie groups. This approach is completely general and can be used to find the infinitesimal generators for any Lie group.
Keywords
Cite
@article{arxiv.2302.05383,
title = {Discovering Sparse Representations of Lie Groups with Machine Learning},
author = {Roy T. Forestano and Konstantin T. Matchev and Katia Matcheva and Alexander Roman and Eyup B. Unlu and Sarunas Verner},
journal= {arXiv preprint arXiv:2302.05383},
year = {2023}
}
Comments
14 pages, 6 figures