Poster
in
Workshop: AI for Science: from Theory to Practice
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin Matchev · Katia Matcheva · Pierre Ramond · Sarunas Verner
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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