English

Neural Networks, Dispersion Relations and the Thermal Bootstrap

High Energy Physics - Theory 2026-05-14 v1

Abstract

We review a framework for the conformal bootstrap that does not rely on positivity and treats the infinite tower of high-dimension OPE contributions to conformal correlators through dispersion relations and neural networks. We apply it to scalar thermal two-point functions on S1×Rd1S^1\times \mathbb R^{d-1}. We discuss the stability properties of the relevant non-convex optimisation scheme and potential relations to recent discussions of smoothness properties in CFT correlators. We illustrate the numerical application of the method to Generalized Free Fields and 4d holographic CFTs. This is a proceedings contribution to the ``Athens Workshop in Theoretical Physics: 10th Anniversary", held at the National and Kapodistrian University of Athens on December 17-19 2025.

Keywords

Cite

@article{arxiv.2605.13183,
  title  = {Neural Networks, Dispersion Relations and the Thermal Bootstrap},
  author = {Vasilis Niarchos and Constantinos Papageorgakis},
  journal= {arXiv preprint arXiv:2605.13183},
  year   = {2026}
}

Comments

21 pages, contribution to the proceedings of the Athens Workshop in Theoretical Physics 2025

R2 v1 2026-07-22T07:09:35.811Z