Radio Frequency Interference

Radio frequency interference (RFI) disrupts data collection across various scientific domains, particularly radio astronomy and communication systems, hindering accurate observations and reliable signal transmission. Current research focuses on developing advanced RFI mitigation techniques using machine learning, employing architectures like spiking neural networks, autoencoders, and convolutional neural networks to detect and remove or restore corrupted signals. These efforts aim to improve data quality, enhance the sensitivity of scientific instruments, and enable more efficient use of the radio frequency spectrum in increasingly crowded environments.