Seeking Truth and Beauty in Flavor Physics with Machine Learning

Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
10/31/2023
hep-phcs.LGmath-ph

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Abstract

The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' criteria like beauty, naturalness, etc. We design loss functions for performing both of those tasks with machine learning techniques. We use the Yukawa quark sector as a toy example to demonstrate that the optimization of these loss functions results in true and beautiful models.

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Cite this paper

@article{matchev2023seeking,
  title  = {Seeking Truth and Beauty in Flavor Physics with Machine Learning},
  author = {Konstantin T. Matchev and Katia Matcheva and Pierre Ramond and Sarunas Verner},
  year   = {2023},
  eprint = {2311.00087},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.00087v1}
}

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