Radio astronomy is crucial for understanding the origin, structure, and evolution of the universe, and for exploring extreme matter states in astrophysical environments. However, radio signals are often disrupted by noise and interference. Detecting and reducing radio frequency interference is vital for maximizing the scientific output of radio telescopes. Traditional methods like Singular Value Decomposition, Principal Component Analysis, Cumsum, and SumThreshold have limitations in handling complex interference. Recently, researchers have combined traditional machine learning with deep learning. Neural networks offer new ideas and tools for future detection of radio frequency interference. This paper discusses the principles, advantages, challenges, and effectiveness of these techniques applied to real astronomical data.
He et al. (Mon,) studied this question.