Gauge covariant neural network for quarks and gluons
We propose gauge covariant neural networks along with a specialized training algorithm for lattice QCD, designed to handle realistic quarks and gluons in four-dimensional space-time. We show that the smearing procedure can be interpreted as an extended version of residual neural networks with fixed parameters. To demonstrate the applicability of our neural networks, we develop a self-learning hybrid Monte Carlo algorithm in the context of two-color QCD, yielding outcomes consistent with those from the conventional hybrid Monte Carlo approach.
Authors
- Yuki Nagai (ORCID: https://orcid.org/0000-0001-5098-5440)
- Akio Tomiya (ORCID: https://orcid.org/0000-0001-9374-3716)
Institutions
- Japan Atomic Energy Agency (JP)
- Brookhaven National Laboratory (US)
- Tokyo Woman's Christian University (JP)
- RIKEN Center for Advanced Intelligence Project (JP)
- RIKEN BNL Research Center (US)
- The University of Tokyo (JP)
Publication Details
- Journal
- Physical review. D/Physical review. D.
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1103/physrevd.111.074501
- Citations
- 3
- Primary Topic
- Quantum Chromodynamics and Particle Interactions
- Type
- article
- Field-Weighted Citation Impact
- 5.61
Funders
- Japan Society for the Promotion of Science
- RIKEN