Seeking Truth and Beauty in Flavor Physics with Machine Learning

• URL: http://arxiv.org/abs/2311.00087v1
• Date: Tue, 31 Oct 2023 18:53:22 GMT
• Title: Seeking Truth and Beauty in Flavor Physics with Machine Learning
• Authors: Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
• Abstract summary: 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.
• Score: 1.8434042562191815
• License: http://creativecommons.org/licenses/by/4.0/
• 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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