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Radio emission from air showers enables measurements of cosmic particlekinematics and identity. The radio signals are detected in broadband Megahertzantennas among continuous background noise. We present two deep learningconcepts and their performance when applied to simulated data. The firstnetwork classifies time traces as signal or background. We achieve a truepositive rate of about 90% for signal-to-noise ratios larger than three with afalse positive rate below 0.2%. The other network is used to clean the timetrace from background and to recover the radio time trace originating from anair shower. Here we achieve a resolution in the energy contained in the traceof about 20% without a bias for $80\%$ of the traces with a signal. Theobtained frequency spectrum is cleaned from signals of radio frequencyinterference and shows the expected shape.

Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning