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PreprintarXiv licencearXiv

Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows

  • Claudio Bonanno
  • Andrea Bulgarelli
  • Elia Cellini
  • Alessandro Nada
  • Dario Panfalone
  • Davide Vadacchino
  • Lorenzo Verzichelli
Open at sourcePDFDOICite

Abstract

In this work we provide numerical evidence that topological freezing in SU(3) Yang-Mills theory can be mitigated with a flow-based approach, as tested at lattice spacings as fine as 0.03~fm. We do so by analyzing the scaling of the costs of a computational strategy based on the combination of open boundary conditions and Stochastic Normalizing Flows. We show that the integrated autocorrelation time of the squared topological charge and the statistical uncertainty on the topological susceptibility remain under control throughout the explored parameter range with currently available computational resources. This marks an important step toward the use of even more efficient flow-based architectures for ergodic sampling of topology in state-of-the-art lattice gauge theory calculations.

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Cite

BibTeXRIS
BibTeX
@misc{bonanno2026ergodic,
  title = {{Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows}},
  author = {Claudio Bonanno and Andrea Bulgarelli and Elia Cellini and Alessandro Nada and Dario Panfalone and Davide Vadacchino and Lorenzo Verzichelli},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.20747},
  eprint = {2610.20747},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.20747},
}
RIS
TY  - UNPB
TI  - Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows
AU  - Claudio Bonanno
AU  - Andrea Bulgarelli
AU  - Elia Cellini
AU  - Alessandro Nada
AU  - Dario Panfalone
AU  - Davide Vadacchino
AU  - Lorenzo Verzichelli
PY  - 2026
DA  - 2026/10/14
PB  - arXiv
DO  - 10.48550/arxiv.2610.20747
UR  - https://arxiv.org/abs/2610.20747
AB  - In this work we provide numerical evidence that topological freezing in SU(3) Yang-Mills theory can be mitigated with a flow-based approach, as tested at lattice spacings as fine as 0.03~fm. We do so by analyzing the scaling of the costs of a computational strategy based on the combination of open boundary conditions and Stochastic Normalizing Flows. We show that the integrated autocorrelation time of the squared topological charge and the statistical uncertainty on the topological susceptibility remain under control throughout the explored parameter range with currently available computational resources. This marks an important step toward the use of even more efficient flow-based architectures for ergodic sampling of topology in state-of-the-art lattice gauge theory calculations.
ER  -