Machine learning in phase transition analysis of lattice quantum gravity
Abstract
A bstract Using numerical data coming from Monte Carlo simulations of four-dimensional Causal Dynamical Triangulations, we study how automated machine learning algorithms can be used to recognize transitions between different phases of quantum geometries observed in lattice quantum gravity. We tested seven supervised and seven unsupervised machine learning models and found that most of them were very successful in that task, even outperforming standard methods based on order parameters.
Fields of Study & Subjects
Noncommutative and Quantum Gravity Theories
Black Holes and Theoretical Physics
Cosmology and Gravitation Theories
Publication details
- Journal / source
- Journal of High Energy Physics
- Publisher
- Springer Science and Business Media LLC
- Volume
- 2026
- Issue
- 7
- Publication date
- 16 July 2026
- Language
- EN