Exploring the Truth and Beauty of Theory Landscapes with Machine Learning

• URL: http://arxiv.org/abs/2401.11513v1
• Date: Sun, 21 Jan 2024 14:52:39 GMT
• Title: Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
• Authors: Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
• Abstract summary: 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 minimization functions whose results in true models that are also beautiful as measured by three different criteria - uniformity, sparsity, or symmetry.
• Score: 1.8434042562191815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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