JOURNAL ARTICLE

Symmetries and interactions of =1 SUGRA: From constructive and BCFW to KLT formulations.

  • Published In: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2025, v. 40, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chakraborty, Dibya; Díaz-Cruz, J. Lorenzo; Pérez, Jonathan Reyes; Ruiz, Pablo Ortega 3 of 3

Abstract

In this paper, we study the couplings of the gravity supermultiplet (graviton and gravitino) of minimal = 1 supergravity (SUGRA) following a constructive approach. First, we use the master formula that follows from considering the scaling behavior of the spinor variables under the little group. Second, we derive the four-point couplings using the Britto–Cachazo–Feng–Witten (known as BCFW) recursion relations. Then, we verify these results for the general three-point interactions that can be derived using the Kawai–Lewellen–Tye relations (also known as KLT-type relations), i.e. they can be written as the square of the coupling of the gluons and gluinos. Finally, we consider the graviton–gravitino SUGRA Compton effect. For completeness, we present in the appendix the = 1 SUGRA Lagrangian in the two-component Weyl formalism, including proofs of supersymmetry (SUSY) and gauge invariances. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 2025/02, Vol. 40, Issue 4, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2025
  • ISSN:0217-751X
  • DOI:10.1142/S0217751X2450177X
  • Accession Number:183710526
  • 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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