Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex

Abstract

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km2. In the present workwe discuss the improvements of the signal reconstruction applied for Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performanceof matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised traces, i.e. removes all signal-unrelated amplitudes. We present the comparison between the standard method of signal reconstruction, matched filtering and the autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection.