论文标题
从具有堆叠自动编码器的极光摩尔辐射中删除射频干扰
Removing Radio Frequency Interference from Auroral Kilometric Radiation with Stacked Autoencoders
论文作者
论文摘要
天文学中的射频数据使科学家能够分析天体物理现象。但是，这些数据可能会因限制了基本自然过程的观察而被射频干扰（RFI）损坏。在这项研究中，我们将深度学习算法的最新发展扩展到天文学数据。我们从含有极光层辐射（AKR）的时频谱谱图中删除RFI，这是一种源自地球的极光区的连贯的无线电发射，用于研究天体物理等离子体。我们提出了一个用于训练有合成频谱图的极光无线电排放器（DAARE）的自动编码器，以降低在南极站收集的AKR信号。 DAARE在合成的AKR观测值上实现了42.2峰值信噪比（PSNR）和0.981结构相似性（SSIM），与最先进的滤波和网络相比，将PSNR提高了3.9，SSIM提高了0.064。定性比较表明，尽管在模拟AKR的数据集中接受了完全训练，但Daare有效地从真实AKR观察中删除RFI的能力。可以在github.com/cylumn/daare上访问模拟AKR，培训daare和使用daare的框架。
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.
