Würzburg 2018 – scientific programme

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T: Fachverband Teilchenphysik

T 21: Experimentelle Methoden der Astroteilchenphysik I

T 21.3: Talk

Monday, March 19, 2018, 16:35–16:50, Z6 - SR 2.013

Deep learning based Extraction of Radio Signals from Extensive Air Showers at the Pierre Auger Observatory — •Felix Schlueter, Martin Erdmann, and Radomir Smida — III. Physikalisches Institut A, RWTH Aachen University, Deutschland

In the recent decade, radio measurements have become a very active field in detection of ultra-high energy cosmic rays. This technique enables a new perspective on the physics of extensive air showers, e.g. with measurements of the absolute energy of cosmic rays with a duty cycle close to 100%.
For every analysis of radio data, noise reduction is a challenge. Radio signals from extensive air showers are contaminated by environmental and human-made noise and can be significantly smaller than the measured noise. In this talk, an approach is presented to reduce noise based on the autoencoder concept used in deep learning techniques. This approach is evaluated using air shower simulations with realistic noise measured by the Auger Engineering Radio Array (AERA). An outlook to data application is given.