Speaker
Konstantin Matchev
(University of Alabama (US))
Description
Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example to demonstrate how both of those tasks can be accomplished with machine learning techniques. We propose loss functions whose minimization results in true models that are also beautiful as measured by three different criteria — uniformity, sparsity, or symmetry.
Authors
Konstantin Matchev
(University of Alabama (US))
Katia Matcheva
(University of Alabama)
Pierre Michel Ramond
(University of Florida (US))
Sarunas Verner
(University of Florida)
