Universal New Physics Latent Space
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929683708248064 |
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| author | Hallin, Anna Kasieczka, Gregor Kraml, Sabine Lessa, André Moureaux, Louis von Schwartz, Tore Shih, David |
| author_facet | Hallin, Anna Kasieczka, Gregor Kraml, Sabine Lessa, André Moureaux, Louis von Schwartz, Tore Shih, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Universal New Physics Latent Space Hallin, Anna Kasieczka, Gregor Kraml, Sabine Lessa, André Moureaux, Louis von Schwartz, Tore Shih, David High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability 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. |
| title | Universal New Physics Latent Space |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2407.20315 |