Smoothing effect and Derivative formulas for Ornstein-Uhlenbeck processes driven by subordinated cylindrical Brownian noises

Abstract

We investigate the concept of cylindrical Wiener process subordinated to a strictly $\alpha$-stable Lévy process, with $\alpha \in \left(0,1\right)$, in an infinite dimensional, separable Hilbert space, and consider the related stochastic convolution. We then introduce the corresponding Ornstein-Uhlenbeck process, focusing on the regularizing properties of the Markov transition semigroup defined by it. In particular, we provide an explicit, original formula -- which is not of Bismut-Elworthy-Li's type -- for the Gateaux derivatives of the functions generated by the operators of the semigroup, as well as an upper bound for the norm of their gradients. In the case $\alpha \in \left(\frac{1}{2},1\right)$, this estimate represents the starting point for studying the Kolmogorov equation in its mild formulation.