  Radio frequency data in astronomy enable scientists to analyze astrophysical
phenomena. However, these data can be corrupted by radio frequency interference
(RFI) that limits the observation of underlying natural processes. In this
study, we extend recent developments in deep learning algorithms to astronomy
data. We remove RFI from time-frequency spectrograms containing auroral
kilometric radiation (AKR), a coherent radio emission originating from the
Earth's auroral zones that is used to study astrophysical plasmas. We propose a
Denoising Autoencoder for Auroral Radio Emissions (DAARE) trained with
synthetic spectrograms to denoise AKR signals collected at the South Pole
Station. DAARE achieves 42.2 peak signal-to-noise ratio (PSNR) and 0.981
structural similarity (SSIM) on synthesized AKR observations, improving PSNR by
3.9 and SSIM by 0.064 compared to state-of-the-art filtering and denoising
networks. Qualitative comparisons demonstrate DAARE's capability to effectively
remove RFI from real AKR observations, despite being trained completely on a
dataset of simulated AKR. The framework for simulating AKR, training DAARE, and
employing DAARE can be accessed at github.com/Cylumn/daare.
