Machine Learning in the 2HDM2S model for Dark Matter
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915955606552576 |
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| author | Boto, Rafael Rebelo, Tiago P. Romão, Jorge C. Silva, João P. |
| author_facet | Boto, Rafael Rebelo, Tiago P. Romão, Jorge C. Silva, João P. |
| contents | We introduce a two real scalar singlet extension of the two Higgs doublet model. We study the vacuum structure, the bounded from below conditions, the restrictions from the oblique parameters S,T and U, as well as the unitarity constraints. We submit the model to collider and Dark Matter experimental constraints and explore its allowed parameter space. We compare randomly populated simulations, simulations starting near the alignment limit, and a Machine Learning based exploration. Using Evolutionary Strategies, we efficiently search for regions with a viable Dark Matter candidate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning in the 2HDM2S model for Dark Matter Boto, Rafael Rebelo, Tiago P. Romão, Jorge C. Silva, João P. High Energy Physics - Phenomenology We introduce a two real scalar singlet extension of the two Higgs doublet model. We study the vacuum structure, the bounded from below conditions, the restrictions from the oblique parameters S,T and U, as well as the unitarity constraints. We submit the model to collider and Dark Matter experimental constraints and explore its allowed parameter space. We compare randomly populated simulations, simulations starting near the alignment limit, and a Machine Learning based exploration. Using Evolutionary Strategies, we efficiently search for regions with a viable Dark Matter candidate. |
| title | Machine Learning in the 2HDM2S model for Dark Matter |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2509.01677 |