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| Full text | |
| Author(s): |
Hallin, Anna
;
Kasieczka, Gregor
;
Kraml, Sabine
;
Lessa, Andre
;
Moureaux, Louis
;
von Schwartz, Tore
;
Shih, David
Total Authors: 7
|
| Document type: | Journal article |
| Source: | PHYSICAL REVIEW D; v. 111, n. 1, p. 17-pg., 2025-01-09. |
| Abstract | |
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology: different models are mapped to distinct regions in latent space, while indistinguishable models are mapped to the same region. This opens interesting new avenues on several fronts, such as model discrimination, selection of representative benchmark scenarios, and identifying gaps in the coverage of model space. (AU) | |
| FAPESP's process: | 21/01089-1 - Cherenkov Telescope Array: construction and first discoveries |
| Grantee: | Luiz Vitor de Souza Filho |
| Support Opportunities: | Special Projects |
| FAPESP's process: | 18/25225-9 - São Paulo Research and Analysis Center |
| Grantee: | Sergio Ferraz Novaes |
| Support Opportunities: | Special Projects |