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Main Authors: Clark, Jeremy, Tsai, Li-Cheng
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.16056
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author Clark, Jeremy
Tsai, Li-Cheng
author_facet Clark, Jeremy
Tsai, Li-Cheng
contents We establish that the family of polymer measures $M^θ_{[s,t]}$ associated with the Stochastic Heat Flow (SHF), indexed by $θ\in\mathbb{R}$, has a conditional Gaussian Multiplicative Chaos (GMC) structure. Namely, taking the random measure $M^θ_{[s,t]}$ as the reference measure, we construct the path-space GMC with noise strength $a > 0$ and prove that the resulting random measure is equal in law to $M^{θ+a}_{[s,t]}$. As two applications, we prove that the polymer measure and SHF tested against general nonnegative functions are almost surely strictly positive and that the SHF converges to $0$ as $θ\to\infty$.
format Preprint
id arxiv_https___arxiv_org_abs_2507_16056
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Conditional GMC within the stochastic heat flow
Clark, Jeremy
Tsai, Li-Cheng
Probability
We establish that the family of polymer measures $M^θ_{[s,t]}$ associated with the Stochastic Heat Flow (SHF), indexed by $θ\in\mathbb{R}$, has a conditional Gaussian Multiplicative Chaos (GMC) structure. Namely, taking the random measure $M^θ_{[s,t]}$ as the reference measure, we construct the path-space GMC with noise strength $a > 0$ and prove that the resulting random measure is equal in law to $M^{θ+a}_{[s,t]}$. As two applications, we prove that the polymer measure and SHF tested against general nonnegative functions are almost surely strictly positive and that the SHF converges to $0$ as $θ\to\infty$.
title Conditional GMC within the stochastic heat flow
topic Probability
url https://arxiv.org/abs/2507.16056