JOURNAL ARTICLE

Geometry from D-branes in nonrelativistic string theory.

  • Published In: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2024, v. 39, n. 17/18. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Güijosa, Alberto; Rosas-López, Igmar C. 3 of 3

Abstract

Nonrelativistic (NR) string theory was discovered as a framework that underlies and unifies the various noncommutative open string (NCOS) theories, which were originally envisioned as surprising exceptions to the maxim that all string theories are gravitational in nature. In that view, the fact that NCOS has a gravitational dual was believed to be directly analogous to the AdS/CFT correspondence. When NCOS theories were understood to be simply the particular classes of states of the underlying NR theory that include longitudinal D-branes, it was suggested that the duality between NCOS and the corresponding gravitational theory is not an instance of gauge/gravity-type duality, but of open-string/closed-string duality between D-branes and black branes. This paper provides direct evidence in support of this perspective, by starting from a stack of D-branes in NR string theory and deriving the long-distance profile of the curved geometry in the corresponding black brane. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 2024/06, Vol. 39, Issue 17/18, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2024
  • ISSN:0217-751X
  • DOI:10.1142/S0217751X24500313
  • Accession Number:178652372
  • Copyright Statement:Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company 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.)

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