Decompositions of log-correlated fields with applications

Janne Junnila*, Eero Saksman, Christian Webb

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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In this article, we establish novel decompositions of Gaussian fields taking values in suitable spaces of generalized functions, and then use these decompositions to prove results about Gaussian multiplicative chaos.

We prove two decomposition theorems. The first one is a global one and says that if the difference between the covariance kernels of two Gaussian fields, taking values in some Sobolev space, has suitable Sobolev regularity, then these fields differ by a Holder continuous Gaussian process. Our second decomposition theorem is more specialized and is in the setting of Gaussian fields whose covariance kernel has a logarithmic singularity on the diagonal-or log-correlated Gaussian fields. The theorem states that any log-correlated Gaussian field X can be decomposed locally into a sum of a Holder continuous function and an independent almost *-scale invariant field (a special class of stationary log-correlated fields with 'cone-like' white noise representations). This decomposition holds whenever the term g in the covariance kernel C-X(x, y) = log(1/vertical bar x - y vertical bar) + g(x, y) has locally Hd+epsilon Sobolev smoothness.

We use these decompositions to extend several results that have been known basically only for *-scale invariant fields to general log-correlated fields. These include the existence of critical multiplicative chaos, analytic continuation of the subcritical chaos in the so-called inverse temperature parameter beta, as well as generalised Onsager-type covariance inequalities which play a role in the study of imaginary multiplicative chaos.

Original languageEnglish
Pages (from-to)3786-3820
Number of pages35
JournalAnnals of Applied Probability
Issue number6
Publication statusPublished - Dec 2019
MoE publication typeA1 Journal article-refereed


  • Log-correlated Gaussian fields
  • random generalized functions
  • Gaussian multiplicative chaos


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