Detecting Radio Frequency Interference in radio-antenna arrays with the Recurrent Neural Network algorithm

Understand

Signal artefacts due to Radio Frequency Interference (RFI) are a common nuisance in radio astronomy.

• Conventionally, the RFI-affected data are tagged by an expert data analyst in order to warrant data quality.
• In view of the increasing data rates obtained with interferometric radio telescope arrays, automatic data filtering procedures are mandatory.
• Here, we present results from the implementation of a RFI-detecting recurrent neural network (RNN) employing long-short term memory (LSTM) cells.