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

• Main Authors: Shipilov, D., Bezyazeekov, P. A., Budnev, N. M., Chernykh, D., Fedorov, O., Gress, O. A., Haungs, A., Hiller, R., Huege, T., Kazarina, Y., Kleifges, M., Korosteleva, E. E., Kostunin, D., Kuzmichev, L. A., Lenok, V., Lubsandorzhiev, N., Marshalkina, T., Monkhoev, R., Osipova, E., Pakhorukov, A., Pankov, L., Prosin, V. V., Schröder, F. G., Zagorodnikov, A.
• Format: Text
• Language: unknown
• Published: arXiv 2018
• Subjects: Instrumentation and Methods for Astrophysics astro-ph.IM; Machine Learning cs.LG; Signal Processing eess.SP; FOS Physical sciences; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; taiga; Siberia
• Online Access: https://dx.doi.org/10.48550/arxiv.1812.03347; https://arxiv.org/abs/1812.03347

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures the radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km\textsuperscript{2}. In the present work we discuss the improvements of the signal reconstruction applied for the 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 performance of 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 trace, i.e. removes all signal-unrelated amplitudes. We present the comparison between standard method of signal reconstruction, matched filtering and autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection. : ARENA2018 proceedings