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The Pierre Auger Observatory, at present the largest cosmic-ray observatoryever built, is instrumented with a ground array of 1600 water-Cherenkovdetectors, known as the Surface Detector (SD). The SD samples the secondaryparticle content (mostly photons, electrons, positrons and muons) of extensiveair showers initiated by cosmic rays with energies ranging from $10^{17}~$eV upto more than $10^{20}~$eV. Measuring the independent contribution of the muoncomponent to the total registered signal is crucial to enhance the capabilityof the Observatory to estimate the mass of the cosmic rays on an event-by-eventbasis. However, with the current design of the SD, it is difficult tostraightforwardly separate the contributions of muons to the SD time tracesfrom those of photons, electrons and positrons. In this paper, we present amethod aimed at extracting the muon component of the time traces registeredwith each individual detector of the SD using Recurrent Neural Networks. Wederive the performances of the method by training the neural network onsimulations, in which the muon and the electromagnetic components of the tracesare known. We conclude this work showing the performance of this method onexperimental data of the Pierre Auger Observatory. We find that our predictionsagree with the parameterizations obtained by the AGASA collaboration todescribe the lateral distributions of the electromagnetic and muonic componentsof extensive air showers.

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks