Lowering In-Memory Footprint of Antenna Beams via Polynomial Approximation

TL;DR

This paper introduces a polynomial approximation method using Zernike polynomials to reduce the in-memory footprint of antenna beam representations, enabling more efficient processing for large radio telescopes like SKA.

Contribution

It presents a novel approach to approximate antenna beams with Zernike polynomials, significantly reducing memory requirements for large-scale radio telescope data processing.

Findings

Beam kernels can be accurately approximated using Zernike polynomials.

The approach facilitates more efficient beam-dependent solutions.

Potential for future optimizations in radio telescope data handling.

Abstract

With the emergence of new radio telescopes promising larger fields of view at lower observation frequencies (e.g., SKA), addressing direction-dependent effects (DDE) (e.g., direction-specific beam responses), polarisation leakage, and pointing errors has become all the more important. Be it through A-projection or otherwise, addressing said effects often requires reliable representations of antenna/station beams; yet, these require significant amounts of computational memory as they are baseline-, frequency-, time-, and polarisation-dependent. A novel prototype is reported here to approximate antenna beams suitable for SKA-MID using Zernike polynomials. It is shown that beam kernels can be well approximated, paving the way for future optimisations towards facilitating more efficient beam-dependent solutions and approaches to tackling the aforementioned challenges, all of which are essential for large-scale radio telescopes.