pyFFS: A Python Library for Fast Fourier Series Computation and Interpolation with GPU Acceleration

Fourier transforms are an often necessary component in many computational tasks, and can be computed efficiently through the fast Fourier transform (FFT) algorithm. However, many applications involve an underlying continuous signal, and a more natural choice would be to work with e.g. the Fourier series (FS) coefficients in order to avoid the additional overhead of translating between the analog and discrete domains. Unfortunately, there exists very little literature and tools for the manipulation of FS coefficients from discrete samples. This paper introduces a Python library called pyFFS for efficient FS coefficient computation, convolution, and interpolation. While the libraries SciPy and NumPy provide efficient functionality for discrete Fourier transform coefficients via the FFT algorithm, pyFFS addresses the computation of FS coefficients through what we call the fast Fourier series (FFS). Moreover, pyFFS includes an FS interpolation method based on the chirp Z-transform that can make it more than an order of magnitude faster than the SciPy equivalent when one wishes to perform interpolation. GPU support through the CuPy library allows for further acceleration, e.g. an order of magnitude faster for computing the 2-D FS coefficients of 1000 x 1000 samples and nearly two orders of magnitude faster for 2-D interpolation. As an application, we discuss the use of pyFFS in Fourier optics. pyFFS is available as an open source package at https://github.com/imagingofthings/pyFFS, with documentation at https://pyffs.readthedocs.io.

Fourier non-uniqueness sets from totally real number fields

Martin Peter Stoller

Let K be a totally real number field of degree n >= 2. The inverse different of K gives rise to a lattice in Rn. We prove that the space of Schwartz Fourier eigenfunctions on R-n which vanish on the "component-wise square root" of this lattice, is infinite dimensional. The Fourier non-uniqueness set thus obtained is a discrete subset of the union of all spheres root mS(n-1) for integers m >= 0 and, as m -> infinity, there are similar to c(K)m(n-1) many points on the m-th sphere for some explicit constant c(K), proportional to the square root of the discriminant of K. This contrasts a recent Fourier uniqueness result by Stoller (2021) Using a different construction involving the codifferent of K, we prove an analogue for discrete subsets of ellipsoids. In special cases, these sets also lie on spheres with more densely spaced radii, but with fewer points on each. We also study a related question about existence of Fourier interpolation formulas with nodes "root Lambda" for general lattices Lambda subset of R-n. Using results about lattices in Lie groups of higher rank we prove that if n >= 2 and a certain group Gamma(Lambda) >= PSL2.(R)(n) is discrete, then such interpolation formulas cannot exist. Motivated by these more general considerations, we revisit the case of one radial variable and prove, for all n >= 5 and all real lambda >= 2, Fourier interpolation results for sequences of spheres root 2m/lambda Sn-1, where m ranges over any fixed cofinite set of non-negative integers. The proof relies on a series of Poincare type for Hecke groups of infinite covolume and is similar to the one in Stoller (2021).

EUROPEAN MATHEMATICAL SOC-EMS2022

Towards More Accurate and Efficient Beamformed Radio Interferometry Imaging

Matthieu Martin Jean-André Simeoni

The Square Kilometre Array (SKA) will form the largest radio telescope ever built, generating on the order of one terabyte of data per second. To reduce the data flow sent to the central processor, hierarchical designs have been proposed: the data is primarily collected in groups of antennas, and summed coherently by beamforming. Historically, Fourier analysis has played a prominent role in radio astronomy interferometry, legitimated by the celebrated van Cittert-Zernike theorem. We show that, in the case of modern hierarchical designs, beamformed data has a less intimate, and thus more complicated relationship to the Fourier domain. Unsatisfactory attempts have been proposed to compensate, which implicitly retain the Fourier framework, and are limited to directive beamforming. We show that when stepping away from Fourier, we can embed the data in a more natural domain originating from the telescope configuration and the specific beamforming technique. This leads to a new, more accurate, imaging pipeline. Standard techniques such as w-projection, and gridding are no longer needed, as the reconstruction is performed on the celestial sphere. The proposed imager operates in two steps. First, a preconditioning based on the Gram-Schmidt orthogonalization procedure is performed, in order to facilitate the computation of the pseudoinverse sky estimate. Then, from this, the LASSO estimate is approximated very efficiently. The quality of this approximation is shown to be linked directly to the effective support of the instrument point spread function. Due to the greater flexibility of this framework, information-maximising beamforming techniques such as randomised beamforming can be readily incorporated. Moreover, we use the Bonferroni method to construct global confidence intervals onto the Gram-Schmidt least squares estimate, and use them to test the statistical significance of each pixel. The complexity of the proposed technique is assessed and compared to the the state-of-the-art combined CLEAN and A-projection algorithm. In the case of LOFAR, we show that our algorithm can be from 2 to 34 times faster. The accuracy and sensitivity of the new technique is also shown, for simulated data, to be superior.

2015

Towards More Accurate and Efficient Beamformed Radio Interferometry Imaging

Matthieu Martin Jean-André Simeoni

2015