@inproceedings{
matchev2023seeking,
title={Seeking Truth and Beauty in Flavor Physics with Machine Learning},
author={Konstantin T. Matchev and Katia Matcheva and Pierre Ramond and Sarunas Verner},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=kOhETZ7SXP}
}
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.
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin T. Matchev Katia Matcheva Pierre Ramond Sarunas Verner