English

Cluster tomography in percolation

Disordered Systems and Neural Networks 2024-02-13 v1 Statistical Mechanics

Abstract

In cluster tomography, we propose measuring the number of clusters NN intersected by a line segment of length \ell across a finite sample. As expected, the leading order of N()N(\ell) scales as aa\ell, where aa depends on microscopic details of the system. However, at criticality, there is often an additional nonlinearity of the form bln()b\ln(\ell), originating from the endpoints of the line segment. By performing large scale Monte Carlo simulations of both 2dd and 3dd percolation, we find that bb is universal and depends only on the angles encountered at the endpoints of the line segment intersecting the sample. Our findings are further supported by analytic arguments in 2dd, building on results in conformal field theory. Being broadly applicable, cluster tomography can be an efficient tool to detect phase transitions and to characterize the corresponding universality class in classical or quantum systems with a relevant cluster structure.

Keywords

Cite

@article{arxiv.2307.04260,
  title  = {Cluster tomography in percolation},
  author = {Helen S. Ansell and Samuel J. Frank and István A. Kovács},
  journal= {arXiv preprint arXiv:2307.04260},
  year   = {2024}
}

Comments

11 pages, 7 figures

R2 v1 2026-06-28T11:25:32.270Z