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Transient radio frequency interference (RFI) is detrimental to radioastronomy. It is particularly difficult to identify the sources of transientRFI, which is broadband and intermittent. Such RFI is often generated bydevices like mechanical relays, fluorescent lighting or AC machines, which maybe present in the surrounding infrastructure of a radio telescope array. Onemitigating approach is to deploy independent RFI monitoring stations at radiotelescope arrays. Once the sources of RFI signals are identified, they may beremoved or replaced where possible. For the first time in the open literature,we demonstrate an approach to classifying the sources of transient RFI (in timedomain data) that makes use of deep learning techniques including CNNs andLSTMs. Applied to a previously obtained dataset of experimentally recordedtransient RFI signals, our proposed approach offers good results. It showspotential for development into a tool for identifying the sources of transientRFI signals recorded by independent RFI monitoring stations.

A CNN and LSTM-Based Approach to Classifying Transient Radio Frequency Interference