Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning
III International Workshop Data life cycle in physics, Irkutsk, Russia, 2 Apr 2019 - 7 Apr 2019; 9 pp. (2019). : The Tunka Radio Extension (Tunka-Rex) is a digital antenna array operating in the frequency band of 30-80 MHz, measuring the radio emission of air-showers induced by ultra-high energy cos...
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Deutsches Elektronen-Synchrotron, DESY, Hamburg
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ftdatacite:10.3204/pubdb-2020-00075 2023-05-15T18:30:51+02:00 Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning Bezyazeekov, P. Budnev, N. Fedorov, O. Gress, O. Grishin, O. Haungs, A. Huege, T. Kazarina, Y. Kleifges, M. Kostunin, D. Korosteleva, E. Kuzmichev, L. Lenok, V. Lubsandorzhiev, N. Malakhov, S. Marshalkina, T. Monkhoev, R. Osipova, E. Pakhorukov, A. Pankov, L. Prosin, V. Schröder, F. G. Shipilov, D. Zagorodnikov, A. 2019 https://dx.doi.org/10.3204/pubdb-2020-00075 http://bib-pubdb1.desy.de/record/434482 en eng Deutsches Elektronen-Synchrotron, DESY, Hamburg Text Report report ScholarlyArticle 2019 ftdatacite https://doi.org/10.3204/pubdb-2020-00075 2021-11-05T12:55:41Z III International Workshop Data life cycle in physics, Irkutsk, Russia, 2 Apr 2019 - 7 Apr 2019; 9 pp. (2019). : The Tunka Radio Extension (Tunka-Rex) is a digital antenna array operating in the frequency band of 30-80 MHz, measuring the radio emission of air-showers induced by ultra-high energy cosmic rays. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them in a densely instrumented area of about 1km2. The signals from the air showers are short pulses, which have a duration of tens of nanoseconds and are recorded in traces of about 5{\mu}s length. The Tunka-Rex analysis of cosmic-ray events is based on the reconstruction of these signals, in particular, their positions in the traces and amplitudes. This reconstruction suffers at low signal-to-noise ratios, i.e. when the recorded traces are dominated by background. To lower the threshold of the detection and increase the efficiency, we apply advanced methods of signal reconstruction, namely matched filtering and deep neural networks with autoencoder architecture. In the present work we show the comparison between the signal reconstructions obtained with these techniques, and give an example of the first reconstruction of the Tunka-Rex signals obtained with a deep neural networks. Report taiga Siberia DataCite Metadata Store (German National Library of Science and Technology) |
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English |
description |
III International Workshop Data life cycle in physics, Irkutsk, Russia, 2 Apr 2019 - 7 Apr 2019; 9 pp. (2019). : The Tunka Radio Extension (Tunka-Rex) is a digital antenna array operating in the frequency band of 30-80 MHz, measuring the radio emission of air-showers induced by ultra-high energy cosmic rays. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them in a densely instrumented area of about 1km2. The signals from the air showers are short pulses, which have a duration of tens of nanoseconds and are recorded in traces of about 5{\mu}s length. The Tunka-Rex analysis of cosmic-ray events is based on the reconstruction of these signals, in particular, their positions in the traces and amplitudes. This reconstruction suffers at low signal-to-noise ratios, i.e. when the recorded traces are dominated by background. To lower the threshold of the detection and increase the efficiency, we apply advanced methods of signal reconstruction, namely matched filtering and deep neural networks with autoencoder architecture. In the present work we show the comparison between the signal reconstructions obtained with these techniques, and give an example of the first reconstruction of the Tunka-Rex signals obtained with a deep neural networks. |
format |
Report |
author |
Bezyazeekov, P. Budnev, N. Fedorov, O. Gress, O. Grishin, O. Haungs, A. Huege, T. Kazarina, Y. Kleifges, M. Kostunin, D. Korosteleva, E. Kuzmichev, L. Lenok, V. Lubsandorzhiev, N. Malakhov, S. Marshalkina, T. Monkhoev, R. Osipova, E. Pakhorukov, A. Pankov, L. Prosin, V. Schröder, F. G. Shipilov, D. Zagorodnikov, A. |
spellingShingle |
Bezyazeekov, P. Budnev, N. Fedorov, O. Gress, O. Grishin, O. Haungs, A. Huege, T. Kazarina, Y. Kleifges, M. Kostunin, D. Korosteleva, E. Kuzmichev, L. Lenok, V. Lubsandorzhiev, N. Malakhov, S. Marshalkina, T. Monkhoev, R. Osipova, E. Pakhorukov, A. Pankov, L. Prosin, V. Schröder, F. G. Shipilov, D. Zagorodnikov, A. Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning |
author_facet |
Bezyazeekov, P. Budnev, N. Fedorov, O. Gress, O. Grishin, O. Haungs, A. Huege, T. Kazarina, Y. Kleifges, M. Kostunin, D. Korosteleva, E. Kuzmichev, L. Lenok, V. Lubsandorzhiev, N. Malakhov, S. Marshalkina, T. Monkhoev, R. Osipova, E. Pakhorukov, A. Pankov, L. Prosin, V. Schröder, F. G. Shipilov, D. Zagorodnikov, A. |
author_sort |
Bezyazeekov, P. |
title |
Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning |
title_short |
Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning |
title_full |
Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning |
title_fullStr |
Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning |
title_full_unstemmed |
Advanced Signal Reconstruction in Tunka-Rex with Matched Filtering and Deep Learning |
title_sort |
advanced signal reconstruction in tunka-rex with matched filtering and deep learning |
publisher |
Deutsches Elektronen-Synchrotron, DESY, Hamburg |
publishDate |
2019 |
url |
https://dx.doi.org/10.3204/pubdb-2020-00075 http://bib-pubdb1.desy.de/record/434482 |
genre |
taiga Siberia |
genre_facet |
taiga Siberia |
op_doi |
https://doi.org/10.3204/pubdb-2020-00075 |
_version_ |
1766214456455462912 |