Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex
arXiv:1812.03347 · doi:10.1051/epjconf/201921602003
Abstract
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.
arXiv commentARENA2018 proceedings
References in corpus (3)
Cited by in corpus (4)
- Ultra-High-Energy Cosmic Rays: The Intersection of the Cosmic and Energy Frontiers
- Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array
- Reconstruction of sub-threshold events of cosmic-ray radio detectors using an autoencoder
- Tunka-Rex Virtual Observatory