On a spectral representation for correlation measures in configuration space analysis
Yu. M. Berezansky, Yu. G. Kondratiev, T. Kuna, E. Lytvynov

TL;DR
This paper develops a spectral representation for correlation measures in configuration space analysis, linking the $K$-transform with a Fourier transform in a Hilbert space framework, enabling the inverse problem of measure reconstruction.
Contribution
It introduces a spectral approach using the projective spectral theorem to analyze correlation measures and connects the $K$-transform with a Fourier transform in a Hilbert space setting.
Findings
Constructed a Fourier transform as a unitary operator
Established the $K$-transform as a Fourier transform
Connected correlation measures with spectral measures of operators
Abstract
The paper is devoted to the study of configuration space analysis by using the projective spectral theorem. For a manifold , let , resp.\ denote the space of all, resp. finite configurations in . The so-called -transform, introduced by A. Lenard, maps functions on into functions on and its adjoint maps probability measures on into -finite measures on . For a probability measure on , is called the correlation measure of . We consider the inverse problem of existence of a probability measure whose correlation measure is equal to a given measure . We introduce an operation of -convolution of two functions on and suppose that the measure is -positive definite, which enables us to introduce the…
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Taxonomy
TopicsMorphological variations and asymmetry · Numerical methods in inverse problems
