SO(8) supergravity and the magic of machine learning
Abstract Using de Wit-Nicolai D = 4 N $$ \mathcal{N} $$ = 8 SO(8) supergravity as an example, we show how modern Machine Learning software libraries such as Google’s TensorFlow can be employed to greatly simplify the analysis of high-dimensional scalar sectors of some M-Theory compactifications. We...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-08-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/JHEP08(2019)057 |
| _version_ | 1818107967290998784 |
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| author | Iulia M. Comsa Moritz Firsching Thomas Fischbacher |
| author_facet | Iulia M. Comsa Moritz Firsching Thomas Fischbacher |
| author_sort | Iulia M. Comsa |
| collection | DOAJ |
| description | Abstract Using de Wit-Nicolai D = 4 N $$ \mathcal{N} $$ = 8 SO(8) supergravity as an example, we show how modern Machine Learning software libraries such as Google’s TensorFlow can be employed to greatly simplify the analysis of high-dimensional scalar sectors of some M-Theory compactifications. We provide detailed information on the location, symmetries, and particle spectra and charges of 192 critical points on the scalar manifold of SO(8) supergravity, including one newly discovered N $$ \mathcal{N} $$ = 1 vacuum with SO(3) residual symmetry, one new potentially stabilizable non-supersymmetric solution, and examples for “Galois conjugate pairs” of solutions, i.e. solution-pairs that share the same gauge group embedding into SO(8) and minimal polynomials for the cosmological constant. Where feasible, we give analytic expressions for solution coordinates and cosmological constants. As the authors’ aspiration is to present the discussion in a form that is accessible to both the Machine Learning and String Theory communities and allows adopting our methods towards the study of other models, we provide an introductory overview over the relevant Physics as well as Machine Learning concepts. This includes short pedagogical code examples. In particular, we show how to formulate a requirement for residual Supersymmetry as a Machine Learning loss function and effectively guide the numerical search towards supersymmetric critical points. Numerical investigations suggest that there are no further supersymmetric vacua beyond this newly discovered fifth solution. |
| first_indexed | 2024-12-11T02:07:52Z |
| format | Article |
| id | doaj.art-79024d13adad4384b7052c07f87a76b7 |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-12-11T02:07:52Z |
| publishDate | 2019-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-79024d13adad4384b7052c07f87a76b72022-12-22T01:24:20ZengSpringerOpenJournal of High Energy Physics1029-84792019-08-012019815710.1007/JHEP08(2019)057SO(8) supergravity and the magic of machine learningIulia M. Comsa0Moritz Firsching1Thomas Fischbacher2Google ResearchGoogle ResearchGoogle ResearchAbstract Using de Wit-Nicolai D = 4 N $$ \mathcal{N} $$ = 8 SO(8) supergravity as an example, we show how modern Machine Learning software libraries such as Google’s TensorFlow can be employed to greatly simplify the analysis of high-dimensional scalar sectors of some M-Theory compactifications. We provide detailed information on the location, symmetries, and particle spectra and charges of 192 critical points on the scalar manifold of SO(8) supergravity, including one newly discovered N $$ \mathcal{N} $$ = 1 vacuum with SO(3) residual symmetry, one new potentially stabilizable non-supersymmetric solution, and examples for “Galois conjugate pairs” of solutions, i.e. solution-pairs that share the same gauge group embedding into SO(8) and minimal polynomials for the cosmological constant. Where feasible, we give analytic expressions for solution coordinates and cosmological constants. As the authors’ aspiration is to present the discussion in a form that is accessible to both the Machine Learning and String Theory communities and allows adopting our methods towards the study of other models, we provide an introductory overview over the relevant Physics as well as Machine Learning concepts. This includes short pedagogical code examples. In particular, we show how to formulate a requirement for residual Supersymmetry as a Machine Learning loss function and effectively guide the numerical search towards supersymmetric critical points. Numerical investigations suggest that there are no further supersymmetric vacua beyond this newly discovered fifth solution.http://link.springer.com/article/10.1007/JHEP08(2019)057Supergravity ModelsSupersymmetry BreakingAdS-CFT CorrespondenceM-Theory |
| spellingShingle | Iulia M. Comsa Moritz Firsching Thomas Fischbacher SO(8) supergravity and the magic of machine learning Journal of High Energy Physics Supergravity Models Supersymmetry Breaking AdS-CFT Correspondence M-Theory |
| title | SO(8) supergravity and the magic of machine learning |
| title_full | SO(8) supergravity and the magic of machine learning |
| title_fullStr | SO(8) supergravity and the magic of machine learning |
| title_full_unstemmed | SO(8) supergravity and the magic of machine learning |
| title_short | SO(8) supergravity and the magic of machine learning |
| title_sort | so 8 supergravity and the magic of machine learning |
| topic | Supergravity Models Supersymmetry Breaking AdS-CFT Correspondence M-Theory |
| url | http://link.springer.com/article/10.1007/JHEP08(2019)057 |
| work_keys_str_mv | AT iuliamcomsa so8supergravityandthemagicofmachinelearning AT moritzfirsching so8supergravityandthemagicofmachinelearning AT thomasfischbacher so8supergravityandthemagicofmachinelearning |