Applications of Machine Learning in Constraining Multi-Scalar Models
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914206159208448 |
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| author | Jurčiukonis, Darius |
| author_facet | Jurčiukonis, Darius |
| contents | Machine learning techniques are used to predict theoretical constraints such as unitarity and boundedness from below in extensions of the Standard Model. This approach has proven effective for models incorporating additional SU(2) scalar multiplets, in particular the quadruplet and sixplet cases. High predictive performance is achieved through the use of suitable neural network architectures and well-prepared training datasets. Moreover, machine learning provides a substantial computational advantage, enabling significantly faster evaluations compared to scalar potential minimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24092 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applications of Machine Learning in Constraining Multi-Scalar Models Jurčiukonis, Darius High Energy Physics - Phenomenology Machine learning techniques are used to predict theoretical constraints such as unitarity and boundedness from below in extensions of the Standard Model. This approach has proven effective for models incorporating additional SU(2) scalar multiplets, in particular the quadruplet and sixplet cases. High predictive performance is achieved through the use of suitable neural network architectures and well-prepared training datasets. Moreover, machine learning provides a substantial computational advantage, enabling significantly faster evaluations compared to scalar potential minimization. |
| title | Applications of Machine Learning in Constraining Multi-Scalar Models |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2509.24092 |