Non-Backtracking Loop Soups and Statistical Mechanics on Spin Networks

Federico Camia, Marcin Lis

Research output: Contribution to journalArticle

Abstract

We introduce and study a Markov field on the edges of a graph G in dimension d≥ 2 whose configurations are spin networks. The field arises naturally as the edge-occupation field of a Poissonian model (a soup) of non-backtracking loops and walks characterized by a spatial Markov property such that, conditionally on the value of the edge-occupation field on a boundary set that splits the graph into two parts, the distributions of the loops and arcs contained in the two parts are independent of each other. The field has a Gibbs distribution with a Hamiltonian given by a sum of terms which involve only edges incident on the same vertex. Its free energy density and other quantities can be computed exactly, and their critical behavior analyzed, in any dimension.

Original languageEnglish (US)
Pages (from-to)403-433
Number of pages31
JournalAnnales Henri Poincare
Volume18
Issue number2
DOIs
StatePublished - Feb 1 2017

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statistical mechanics
Statistical Mechanics
occupation
Gibbs Distribution
Markov Property
Graph in graph theory
Critical Behavior
apexes
Energy Density
flux density
arcs
Walk
free energy
Free Energy
Arc of a curve
configurations
Configuration
Term
Vertex of a graph

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Nuclear and High Energy Physics
  • Mathematical Physics

Cite this

Non-Backtracking Loop Soups and Statistical Mechanics on Spin Networks. / Camia, Federico; Lis, Marcin.

In: Annales Henri Poincare, Vol. 18, No. 2, 01.02.2017, p. 403-433.

Research output: Contribution to journalArticle

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