Abstract:We study a class of spectral multipliers φ(L) for the Ornstein-Uhlenbeck operator L arising from the Gaussian measure on R n and find a sufficient condition for integrability of φ(L)f in terms of the admissible conical square function and a maximal function.2010 Mathematics Subject Classification. 42B25 (Primary); 42B15, 42B30 (Secondary).
This paper concerns harmonic analysis of the Ornstein-Uhlenbeck operator L on the Euclidean space. We examine the method of decomposing a spectral multiplier φ(L) into three parts according to the notion of admissibility, which quantifies the doubling behaviour of the underlying Gaussian measure γ. We prove that the abovementioned admissible decomposition is bounded in L p (γ) for 1 < p ≤ 2 in a certain sense involving the Gaussian conical square function. The proof relates admissibility with E. Nelson's hypercontractivity theorem in a novel way.2010 Mathematics Subject Classification. 42B25 (Primary); 42B15, 42B30 (Secondary).
This paper concerns harmonic analysis of the Ornstein-Uhlenbeck operator L on the Euclidean space. We examine the method of decomposing a spectral multiplier φ(L) into three parts according to the notion of admissibility, which quantifies the doubling behaviour of the underlying Gaussian measure γ. We prove that the abovementioned admissible decomposition is bounded in L p (γ) for 1 < p ≤ 2 in a certain sense involving the Gaussian conical square function. The proof relates admissibility with E. Nelson's hypercontractivity theorem in a novel way.2010 Mathematics Subject Classification. 42B25 (Primary); 42B15, 42B30 (Secondary).
Abstract. This paper presents a closed-form expression for the integral kernels associated with the derivatives of the Ornstein-Uhlenbeck semigroup e tL . Our approach is to expand the Mehler kernel into Hermite polynomials and applying the powers L N of the Ornstein-Uhlenbeck operator to it, where we exploit the fact that the Hermite polynomials are eigenfunctions for L. As an application we give an alternative proof of the kernel estimates by [11], making all relevant quantities explicit.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.