$\texttt{MEDEA}$: A New Model for Emulating Radio Antenna Beam Patterns for 21-cm Cosmology and Antenna Design Studies

TL;DR

MEDEA is a fast, accurate emulator for radio antenna beam patterns that aids in 21-cm cosmology by efficiently exploring hyper-parameter spaces and fitting mock data.

Contribution

It introduces a novel linear basis interpolation method to rapidly generate antenna beam patterns across hyper-parameters, reducing computational costs.

Findings

Achieves RMS relative errors of at most 1% with 20 input beams

Successfully recovers input hyper-parameters from mock data

Identifies biases when systematic errors are present in data

Abstract

In 21-cm experimental cosmology, accurate characterization of a radio telescope's antenna beam response is essential to measure the 21-cm signal. Computational electromagnetic (CEM) simulations estimate the antenna beam pattern and frequency response by subjecting the EM model to different dependencies, or beam hyper-parameters, such as soil dielectric constant or orientation with the environment. However, it is computationally expensive to search all possible parameter spaces to optimize the antenna design or accurately represent the beam to the level required for use as a systematic model in 21-cm cosmology. We therefore present $\texttt{MEDEA}$, an emulator which rapidly and accurately generates farfield radiation patterns over a large hyper-parameter space. $\texttt{MEDEA}$ takes a subset of beams simulated by CEM software, spatially decomposes them into coefficients in a complete, linear basis, and then interpolates them to form new beams at arbitrary hyper-parameters. We test $\texttt{MEDEA}$ on an analytical dipole and two numerical beams motivated by upcoming lunar lander missions, and then employ $\texttt{MEDEA}$ as a model to fit mock radio spectrometer data to extract covariances on the input beam hyper-parameters. We find that the interpolated beams have RMS relative errors of at most $10^{-2}$ using 20 input beams or less, and that fits to mock data are able to recover the input beam hyper-parameters when the model and mock derive from the same set of beams. When a systematic bias is introduced into the mock data, extracted beam hyper-parameters exhibit bias, as expected. We propose several future extensions to $\texttt{MEDEA}$ to potentially account for such bias.