Radio Frequency Interference (RFI) is a type of inevitable noise in the radio astronomy data collection process. It can corrupt weak cosmic signals and potentially lead to misleading results. The proper identification of RFI during data processing thus is critical to obtain clean and high-quality data for analysis. This need will become even more urgent when next generation of large radio telescopes, such as the Five-hundred-meter Aperture Spherical radio Telescope (FAST) comes into service and generates an increasing amount and complexity of radio signal data. Among RFI identification methods, detection using artificial intelligence (AI) has particularly demonstrated advantages in superior efficiency, accuracy and less human intervention. We thus propose a RFI detection model based on Pix2Pix, an image-to-image translation solver using Generative Adversarial Network (GAN): RFI-GAN. This model transforms the RFI detection to an image translation problem and trains two deep neural networks that contest each other to output a binary RFI mask image to eliminate RFI noises. We also optimize the network structures of the generator and discriminator used in the Pix2Pix model for better quality of RFI detection, making it suitable for processing data from single antenna radio telescope. The model is designed to serve the upcoming FAST data and has been evaluated using a standard simulation data set generated by the HI Data Emulator (HIDE). Experimental results have shown that our model can achieve higher scores (99%) on accuracy, recall and F1-score than other RFI detection methods.

无线电频率干扰 (RFI) 是射电天文数据收集过程中不可避免的一种噪声。它会破坏微弱的宇宙信号，并可能导致误导性结果。因此，在数据处理过程中正确识别 RFI 对于获得干净和高质量的数据进行分析至关重要。当下一代大型射电望远镜，如五百米口径球面射电望远镜 (FAST) 投入使用并产生越来越多的射电信号数据时，这种需求将变得更加紧迫。在 RFI 识别方法中，使用人工智能 (AI) 的检测在效率高、准确度高和人为干预少等方面尤其具有优势。因此，我们提出了一种基于Pix2Pix的 RFI 检测模型，使用生成对抗网络 (GAN) 的图像到图像转换求解器：RFI-GAN。该模型将 RFI 检测转换为图像翻译问题，并训练两个相互竞争的深度神经网络，以输出二值 RFI 掩模图像以消除 RFI 噪声。我们还优化了Pix2Pix模型中使用的生成器和鉴别器的网络结构，以获得更好的 RFI 检测质量，使其适合处理来自单天线射电望远镜的数据。该模型旨在为即将到来的 FAST 数据提供服务，并已使用由 HI 数据仿真器 ( HIDE)生成的标准仿真数据集进行评估）。实验结果表明，与其他 RFI 检测方法相比，我们的模型可以在准确率、召回率和 F1 分数上获得更高的分数（99%）。