Seeking Truth and Beauty in Flavor Physics with Machine Learning

Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner

Keywords: Theoretical physics, machine learning, scientific discovery, model building, data analysis

TL;DR: We seek truth and beauty with machine learning in flavor physics

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.

Submission Track: Original Research

Submission Number: 52