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Quiver mutations, Seiberg duality, and machine learning

Bao, J. ORCID: 0000-0002-9583-1696, Franco, S., He, Y-H. ORCID: 0000-0002-0787-8380, Hirst, E. ORCID: 0000-0003-1699-4399, Musiker, G. and Xiao, Y. (2020). Quiver mutations, Seiberg duality, and machine learning. Physical Review D (PRD), 102(8), 086013.. doi: 10.1103/PhysRevD.102.086013

Abstract

We initiate the study of applications of machine learning to Seiberg duality, focusing on the case of quiver gauge theories, a problem also of interest in mathematics in the context of cluster algebras. Within the general theme of Seiberg duality, we define and explore a variety of interesting questions, broadly divided into the binary determination of whether a pair of theories picked from a series of duality classes are dual to each other, as well as the multiclass determination of the duality class to which a given theory belongs. We study how the performance of machine learning depends on several variables, including number of classes and mutation type (finite or infinite). In addition, we evaluate the relative advantages of Naive Bayes classifiers versus convolutional neural networks. Finally, we also investigate how the results are affected by the inclusion of additional data, such as ranks of gauge/flavor groups and certain variables motivated by the existence of underlying Diophantine equations. In all questions considered, high accuracy and confidence can be achieved.

Publication Type: Article
Additional Information: Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP. .
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date Deposited: 23 Apr 2021 13:16
URI: https://openaccess.city.ac.uk/id/eprint/25972
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