Statistics for random representations of Lie algebras

In this paper we investigate how a typical, large-dimensional representation looks for a complex Lie algebra. In particular, we study the family $\mathfrak{sl}_{r+1}(\mathbb{C})$ of Lie algebras for $r \geq 2$ and derive asymptotic probability distributions for the multiplicity of small irreducible representations, as well as the largest dimension, the largest height, and the total number of irreducible representations appearing in the decomposition of a representation sampled uniformly from all representations with the same dimension. This provides a natural generalization to the similar statistical studies of integer partitions, which forms the case $r=1$ of our considerations and where one has a rich toolkit ranging from combinatorial methods to approaches utilizing the theory of modular forms. We perform our analysis by extending the statistical mechanics inspired approaches in the case of partitions to the infinite family here.

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Publication Details

Journal
ArXiv.org
Published
2025-03-04
Primary Topic
Bayesian Methods and Mixture Models
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article
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article

Statistics for random representations of Lie algebras

Walter Bridges, Caner Nazaroglu, Kathrin Bringmann
ArXiv.org
Bayesian Methods and Mixture Models
article

Statistics for random representations of Lie algebras

Walter Bridges, Caner Nazaroglu, Kathrin Bringmann
article en

Abstract

In this paper we investigate how a typical, large-dimensional representation looks for a complex Lie algebra. In particular, we study the family $\mathfrak{sl}_{r+1}(\mathbb{C})$ of Lie algebras for $r \geq 2$ and derive asymptotic probability distributions for the multiplicity of small irreducible representations, as well as the largest dimension, the largest height, and the total number of irreducible representations appearing in the decomposition of a representation sampled uniformly from all representations with the same dimension. This provides a natural generalization to the similar statistical studies of integer partitions, which forms the case $r=1$ of our considerations and where one has a rich toolkit ranging from combinatorial methods to approaches utilizing the theory of modular forms. We perform our analysis by extending the statistical mechanics inspired approaches in the case of partitions to the infinite family here.

ArXiv.org
Openalex Percentile: Top 84%
Bayesian Methods and Mixture Models
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