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Quark Mass Models and Reinforcement Learning.
- Source :
-
Journal of High Energy Physics . Aug2021, Vol. 2021 Issue 8, p1-21. 21p. - Publication Year :
- 2021
-
Abstract
- In this paper, we apply reinforcement learning to the problem of constructing models in particle physics. As an example environment, we use the space of Froggatt-Nielsen type models for quark masses. Using a basic policy-based algorithm we show that neural networks can be successfully trained to construct Froggatt-Nielsen models which are consistent with the observed quark masses and mixing. The trained policy networks lead from random to phenomenologically acceptable models for over 90% of episodes and after an average episode length of about 20 steps. We also show that the networks are capable of finding models proposed in the literature when starting at nearby configurations. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PARTICLE physics
*ALGORITHMS
*QUARKS
*QUARK models
*REINFORCEMENT learning
Subjects
Details
- Language :
- English
- ISSN :
- 11266708
- Volume :
- 2021
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Journal of High Energy Physics
- Publication Type :
- Academic Journal
- Accession number :
- 152501837
- Full Text :
- https://doi.org/10.1007/JHEP08(2021)161