Regularized Master-Field Approximation for Mass-Deformed Supersymmetric Yang-Mills Matrix Models

The regularized master-field approximation (RMA) is an efficient computational method for large-$N$ reduced matrix models, including those with multiple matrices or complex integration weights. In this work, we apply the RMA to mass-deformed supersymmetric Yang--Mills-type matrix models with three, four, and ten matrices. All three models admit fuzzy-sphere configurations as classical solutions. We show that the RMA quantitatively reproduces these fuzzy-sphere solutions and their breakdown, and that zero modes of the Dirac operator can destabilize fuzzy-sphere configurations in certain cases.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-29
DOI
https://doi.org/10.48550/arxiv.2610.39101
Primary Topic
Black Holes and Theoretical Physics
Type
preprint
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preprint

Regularized Master-Field Approximation for Mass-Deformed Supersymmetric Yang-Mills Matrix Models

Reishi Maeta, Yue Lei
arXiv (Cornell University)
Black Holes and Theoretical Physics
preprint

Regularized Master-Field Approximation for Mass-Deformed Supersymmetric Yang-Mills Matrix Models

Reishi Maeta, Yue Lei
preprint en

Abstract

The regularized master-field approximation (RMA) is an efficient computational method for large-$N$ reduced matrix models, including those with multiple matrices or complex integration weights. In this work, we apply the RMA to mass-deformed supersymmetric Yang--Mills-type matrix models with three, four, and ten matrices. All three models admit fuzzy-sphere configurations as classical solutions. We show that the RMA quantitatively reproduces these fuzzy-sphere solutions and their breakdown, and that zero modes of the Dirac operator can destabilize fuzzy-sphere configurations in certain cases.

arXiv (Cornell University)
Black Holes and Theoretical Physics
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Regularized Master-Field Approximation for Mass-Deformed Supersymmetric Yang-Mills Matrix Models — Reishi Maeta, Yue Lei · arXiv (Cornell University) (2026) | TGRS Research Map | TGRS