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

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Bulletin of the Russian Academy of Sciences: Physics Aims and scope

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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ACKNOWLEDGMENTS

This work was performed on equipment at the Tunka Astrophysical Center under an agreement with the RF Ministry of Education and Science, unique identifier RFMEFI59317X0005.

Funding

This work was supported by the RF Ministry of Education and Science as part of State Tasks 3.9678.2017/BCh, 3.904.2017/PCh, 3.6787.2017/ITR, and 1.6790.2017/ITR; and by the Russian Foundation for Basic Research, project nos. 16-02-00738, 17-02-00905, and 18-32-00460.

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Authors and Affiliations

  1. Institute of Applied Physics, Irkutsk State University, 664003, Irkutsk, Russia

    T. N. Marshalkina, P. A. Bezyazeekov, N. M. Budnev, D. O. Chernykh, O. L. Fedorov, O. A. Gress, Y. A. Kazarina, R. R. Mirgazov, R. Monkhoev, A. L. Pakhorukov, L. V. Pankov, F. G. Schröder, D. A. Shipilov & A. V. Zagorodnikov

  2. Institut für Kernphysik, Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany

    A. Haungs, R. Hiller, T. Huege, D. G. Kostunin & V. V. Lenok

  3. Institut für Prozessdatenverarbeitung und Elektronik, Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany

    M. Kleifges

  4. Skobeltsyn Institute of Nuclear Physics, Moscow State University, 119991, Moscow, Russia

    E. E. Korosteleva, L. A. Kuzmichev, N. B. Lubsandorzhiev, E. A. Osipova & V. V. Prosin

  5. Department of Physics and Astronomy, University of Delaware, DE 19716, Newark, USA

    F. G. Schröder

Authors
  1. T. N. Marshalkina
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  2. P. A. Bezyazeekov
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  3. N. M. Budnev
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  4. D. O. Chernykh
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  5. O. L. Fedorov
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  6. O. A. Gress
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  7. A. Haungs
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  8. R. Hiller
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  9. T. Huege
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  10. Y. A. Kazarina
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  11. M. Kleifges
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  12. D. G. Kostunin
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  13. E. E. Korosteleva
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  14. L. A. Kuzmichev
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  15. V. V. Lenok
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  16. N. B. Lubsandorzhiev
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  17. R. R. Mirgazov
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  18. R. Monkhoev
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  19. E. A. Osipova
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  20. A. L. Pakhorukov
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  21. L. V. Pankov
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  22. V. V. Prosin
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  23. F. G. Schröder
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  24. D. A. Shipilov
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  25. A. V. Zagorodnikov
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Corresponding author

Correspondence to T. N. Marshalkina.

Additional information

Translated by M. Chubarova

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Cite this article

Marshalkina, T.N., Bezyazeekov, P.A., Budnev, N.M. et al. Improving Signal Reconstruction with Matched Filters and Neural Networks for the Tunka-Rex Experiment. Bull. Russ. Acad. Sci. Phys. 83, 1013–1015 (2019). https://doi.org/10.3103/S1062873819080276

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  • DOI: https://doi.org/10.3103/S1062873819080276

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