Efficient Computation of the Zeros of the Bargmann Transform Under Additive White Noise.

We study the computation of the zero set of the Bargmann transform of a signal contaminated with complex white noise, or, equivalently, the computation of the zeros of its short-time Fourier transform with Gaussian window. We introduce the adaptive minimal grid neighbors algorithm (AMN), a variant of a method that has recently appeared in the signal processing literature, and prove that with high probability it computes the desired zero set. More precisely, given samples of the Bargmann transform of a signal on a finite grid with spacing δ, AMN is shown to compute the desired zero set up to a factor of δ in the Wasserstein error metric, with failure probability O(δ4log2(1/δ)). We also provide numerical tests and comparison with other algorithms.

Multiple Sampling and Interpolation in Weighted Fock Spaces of Entire Functions.

We characterize sampling and interpolating sets with derivatives in weighted Fock spaces on the complex plane in terms of their weighted Beurling densities.