SciPost Submission Page
Applications of Machine Learning in Constraining Multi-Scalar Models
by Darius Jurčiukonis
Submission summary
| Authors (as registered SciPost users): | Darius Jurčiukonis |
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| Preprint Link: | https://arxiv.org/abs/2509.24092v1 (pdf) |
| Code repository: | https://github.com/jurciukonis/ML-for-multiples |
| Date submitted: | Oct. 1, 2025, 10:14 a.m. |
| Submitted by: | Darius Jurčiukonis |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
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| Academic field: | Physics |
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| Approach: | Phenomenological |
Abstract
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.
Current status:
In refereeing