https://doi.org/10.1051/epjconf/201921602003
Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex
1
Institute of Applied Physics ISU, Irkutsk, Russia
2
Institut für Kernphysik, Karlsruhe Institute of Technology (KIT),
Karlsruhe, Germany
3
Institut für Prozessdatenverarbeitung und Elektronik, Karlsruhe Institute of Technology (KIT),
Karlsruhe, Germany
4
Skobeltsyn Institute of Nuclear Physics MSU, Moscow, Russia
5
Department of Physics and Astronomy, University of Delaware
Newark, DE, USA
★ now at the University of Zürich
★★ also at Vrije Universiteit Brussel, Brussels, Belgium
Published online: 24 September 2019
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.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
