English

An approach to sub-Gaussian heat kernel estimates via analysis on metric spaces

Probability 2025-10-08 v2 Functional Analysis

Abstract

In this work, we establish a new characterization of sub-Gaussian heat kernel estimates for strongly local regular Dirichlet forms on metric measure spaces. Our formulation is based on the newly introduced cutoff energy condition, which offers a simpler and more transparent alternative for earlier technical energy inequalities, in particular the cutoff Sobolev inequality. The main idea of our approach is to reinterpret the cutoff Sobolev inequality as a Poincar\'e type inequality, and analyze it using Haj{\l}asz--Koskela techniques from analysis on metric spaces. Applications of the new characterization are also discussed.

Keywords

Cite

@article{arxiv.2509.04155,
  title  = {An approach to sub-Gaussian heat kernel estimates via analysis on metric spaces},
  author = {Riku Anttila},
  journal= {arXiv preprint arXiv:2509.04155},
  year   = {2025}
}

Comments

37 pages, comments are welcome! Revision: The main results have been generalized to cover cases where the ambient space is not necessarily geodesic. A brief discussion on applications to reflected diffusion has been included