Search

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 hyperparameters, 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 the Model for Emulating Directivities and Electric fields of Antennas ( MEDEA) , an emulator that rapidly and accurately generates far-field radiation patterns over a large hyperparameter space. MEDEA takes a subset of beams simulated by CEM software, spatially decomposes them into coefficients on a complete, linear basis, and then interpolates them to form new beams at arbitrary hyperparameters. We test MEDEA on an analytical dipole and two numerical beams motivated by upcoming lunar lander missions, and then employ MEDEA as a model to fit mock radio spectrometer data to extract covariances on the input beam hyperparameters. 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 hyperparameters when the model and mock are derived from the same set of beams. When a systematic bias is introduced into the mock data, extracted beam hyperparameters exhibit bias, as expected. We propose several extensions to MEDEA to potentially account for such bias.

Accurate detection of the cosmological 21 cm global signal requires galactic foreground models that can remove power over 10 ^6 . Although foreground and global signal models unavoidably exhibit overlap in their vector spaces inducing bias error in the extracted signal, a second source of bias and error arises from inadequate foreground models, i.e., models that cannot fit spectra down to the noise level of the signal. We therefore test the level to which seven commonly employed foreground models—including nonlinear and linear forward models, polynomials, and maximally smooth polynomials—fit realistic simulated mock foreground spectra, as well as their dependence upon model inputs. The mock spectra are synthesized for an EDGES-like experiment and we compare all models’ goodness of fit and preference using a Kolmogorov–Smirnov (K-S) test of the noise-normalized residuals in order to compare models with differing, and sometimes indeterminable, degrees of freedom. For a single local sidereal time (LST) bin spectrum and p -value threshold of p = 0.05, the nonlinear forward model with four parameters is preferred ( p = 0.99), while the linear forward model fits well with six to seven parameters ( p = 0.94, 0.97, respectively). The polynomials and maximally smooth polynomials, like those employed by the EDGES and SARAS3 experiments, cannot produce good fits with five parameters for the experimental simulations in this work ( p < 10 ^−6 ). However, we find that polynomials with six parameters pass the K-S test ( p = 0.4), although a nine-parameter fit produces the highest p -value ( p ∼ 0.67). When fitting multiple LST bins simultaneously, we find that the linear forward model outperforms (a higher p -value) the nonlinear model for 2, 5, and 10 LST bins. Importantly, the K-S test consistently identifies best-fit and preferred models.