Improving Signal Reconstruction with Matched Filters and Neural Networks for the Tunka-Rex Experiment

T. N. Marshalkina; P. A. Bezyazeekov; N. M. Budnev; D. O. Chernykh; O. L. Fedorov; O. A. Gress; A. Haungs; R. Hiller; T. Huege; Y. A. Kazarina; M. Kleifges; D. G. Kostunin; E. E. Korosteleva; L. A. Kuzmichev; V. V. Lenok; N. B. Lubsandorzhiev; R. R. Mirgazov; R. Monkhoev; E. A. Osipova; A. L. Pakhorukov; L. V. Pankov; V. V. Prosin; F. G. Schröder; D. A. Shipilov; A. V. Zagorodnikov

doi:10.3103/S1062873819080276

Improving Signal Reconstruction with Matched Filters and Neural Networks for the Tunka-Rex Experiment

Authors: Marshalkina T.N.¹, Bezyazeekov P.A.¹, Budnev N.M.¹, Chernykh D.O.¹, Fedorov O.L.¹, Gress O.A.¹, Haungs A.², Hiller R.², Huege T.², Kazarina Y.A.¹, Kleifges M.³, Kostunin D.G.², Korosteleva E.E.⁴, Kuzmichev L.A.⁴, Lenok V.V.², Lubsandorzhiev N.B.⁴, Mirgazov R.R.¹, Monkhoev R.¹, Osipova E.A.⁴, Pakhorukov A.L.¹, Pankov L.V.¹, Prosin V.V.⁴, Schröder F.G.¹^,5, Shipilov D.A.¹, Zagorodnikov A.V.¹
Affiliations:
1. Institute of Applied Physics, Irkutsk State University
2. Institut für Kernphysik, Karlsruhe Institute of Technology (KIT)
3. Institut für Prozessdatenverarbeitung und Elektronik, Karlsruhe Institute of Technology (KIT)
4. Skobeltsyn Institute of Nuclear Physics, Moscow State University
5. Department of Physics and Astronomy, University of Delaware
Issue: Vol 83, No 8 (2019)
Pages: 1013-1015
Section: Article
URL: https://journals.rcsi.science/1062-8738/article/view/187503
DOI: https://doi.org/10.3103/S1062873819080276
ID: 187503

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Abstract

Tunka-Rex is an antenna array for the detection of radio emissions from extensive air showers generated by ultra–high energy cosmic rays. This emission has a broadband spectrum, which corresponds to pulses with durations of tens of nanoseconds and is measured in the band of 30 to 80 MHz. Matched filtering and artificial neural networks are used to improve signal processing at the Tunka-Rex facility. Matched filtering allows more accurate determination the signal peak time, but the best performance can only be achieved with white noise. Convolutional neural networks with autoencoder architecture are used to improve recognition of noise features in traces. These are implemented in Tunka-Rex signal processing and their performance is compared to that of standard means.

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Improving Signal Reconstruction with Matched Filters and Neural Networks for the Tunka-Rex Experiment

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Abstract

About the authors

Supplementary files