Approximate Gibbsian structure in strongly correlated point fields and generalized Gaussian zero ensembles
Ujan Gangopadhyay, Subhro Ghosh, Kin Aun Tan

TL;DR
This paper develops a framework for approximate Gibbsian structures in strongly correlated random point fields, including Gaussian zero ensembles, revealing their rigidity and local interaction properties.
Contribution
It introduces a general approach to approximate Gibbs properties in strongly dependent point fields, extending Gibbsian analysis to complex, highly correlated processes like Gaussian zero ensembles.
Findings
General framework for approximate Gibbsian structure in strongly correlated fields
Established generalized Gibbs property for $eta$-GAF zero processes
Demonstrated rigidity levels and Coulomb-type interactions in these processes
Abstract
Gibbsian structure in random point fields has been a classical tool for studying their spatial properties. However, exact Gibbs property is available only in a relatively limited class of models, and it does not adequately address many random fields with a strongly dependent spatial structure. In this work, we provide a general framework for approximate Gibbsian structure for strongly correlated random point fields. These include processes that exhibit strong spatial rigidity, in particular, a certain one-parameter family of analytic Gaussian zero point fields, namely the -GAFs. Our framework entails conditions that may be verified via finite particle approximations to the process, a phenomenon that we call an approximate Gibbs property. We show that these enable one to compare the spatial conditional measures in the infinite volume limit with Gibbs-type densities supported on…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Material Dynamics and Properties · Theoretical and Computational Physics
