JOURNAL ARTICLE

Large N limit of fuzzy geometries coupled to fermions.

  • Published In: Journal of Mathematical Physics, 2025, v. 66, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Khalkhali, Masoud; Pagliaroli, Nathan; Verhoeven, Luuk S. 3 of 3

Abstract

This article analyzes the large N limit of fermionic quartic Dirac ensembles based on (0, 1)-fuzzy geometries, which are single-matrix, multi-trace Hermitian matrix ensembles incorporating fermionic contributions. The authors prove the existence and uniqueness of the large N spectral density (equilibrium measure) for these multi-tracial, non-polynomial matrix models and develop spectral estimators—specifically spectral dimension and spectral variance—to extract geometric information from the normalized heat kernel, as the usual heat kernel techniques are inapplicable due to bounded spectra. They investigate the effects of adding a fermionic action with mass terms on the spectral density, phase transitions between one-cut and two-cut spectral phases, and spectral estimators, showing that fermionic contributions modify eigenvalue repulsion and coupling constants, thereby influencing the geometry encoded by the Dirac operator. The paper also discusses extensions to multiple fermions and connections to random matrix theory and noncommutative geometry, providing integral equations for equilibrium measures and numerical analyses of spectral properties under varying model parameters.

Additional Information

  • Source:Journal of Mathematical Physics. 2025/05, Vol. 66, Issue 5, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2025
  • ISSN:0022-2488
  • DOI:10.1063/5.0220645
  • Accession Number:185593428
  • Copyright Statement:Copyright of Journal of Mathematical Physics is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.