Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder
Cosmic rays were discovered by the Austrian physicist Victor Hess in 1912 in a series of balloon experiments performed between 1911 and 1912. Cosmic rays are an integral part of fundamental and applied research in the field of solar–terrestrial physics and space weather. Cosmic ray data are applied...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/4/744 |
| _version_ | 1797443480957485056 |
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| author | Oksana Mandrikova Bogdana Mandrikova |
| author_facet | Oksana Mandrikova Bogdana Mandrikova |
| author_sort | Oksana Mandrikova |
| collection | DOAJ |
| description | Cosmic rays were discovered by the Austrian physicist Victor Hess in 1912 in a series of balloon experiments performed between 1911 and 1912. Cosmic rays are an integral part of fundamental and applied research in the field of solar–terrestrial physics and space weather. Cosmic ray data are applied in different fields from the discovery of high-energy particles coming to Earth from space, and new fundamental symmetries in the laws of nature, to the knowledge of residual matter and magnetic fields in interstellar space. The properties of interplanetary space are determined from intensity variations, angular distribution, and other characteristics of galactic cosmic rays. The measure of cosmic ray flux intensity variability is used as one of the significant space weather factors. The negative impact of cosmic rays is also known. The negative impact can significantly increase the level of radiation hazard and pose a threat to astronauts, crews, and passengers of high-altitude aircraft on polar routes and to modern space equipment. Therefore, methods aimed at timely detection and identification of anomalous manifestations in cosmic rays are of particular practical relevance. The article proposes a method for analyzing cosmic ray variations and detecting anomalous changes in the rate of galactic cosmic ray arrival to the Earth. The method is based on a combination of the Autoencoder neural network with wavelet transform. The use of non-linear activation functions and the ability to flexibly change the structure of the network provide the ability of the Autoencoder to approximate complex dependencies in the recorded variations of cosmic rays. The article describes the numerical operations of the method implementation. Verification of the adequacy of the neural network model is based on the use of Box–Ljung Q-statistics. On the basis of the wavelet transform constructions, data-adaptive operations for detecting complex singular structures are constructed. The parameters of the applied threshold functions are estimated with a given confidence probability based on the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-quantiles of Student’s distribution. Using data from high-latitude neutron monitor stations, it is shown that the proposed method provides efficient detection of anomalies in cosmic rays during increased solar activity and magnetic storms. Using the example of a moderate magnetic storm on 10–11 May 2019, the necessity of applying different methods and approaches to the study of cosmic ray variations is confirmed, and the importance of taking them into account when making space weather forecast is shown. |
| first_indexed | 2024-03-09T12:56:40Z |
| format | Article |
| id | doaj.art-2cab5c7c6ed54aa386c9b92d8283e528 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T12:56:40Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-2cab5c7c6ed54aa386c9b92d8283e5282023-11-30T21:59:21ZengMDPI AGSymmetry2073-89942022-04-0114474410.3390/sym14040744Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks AutoencoderOksana Mandrikova0Bogdana Mandrikova1Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences, Mirnaya st, 7, Paratunka, 684034 Kamchatskiy Kray, RussiaInstitute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences, Mirnaya st, 7, Paratunka, 684034 Kamchatskiy Kray, RussiaCosmic rays were discovered by the Austrian physicist Victor Hess in 1912 in a series of balloon experiments performed between 1911 and 1912. Cosmic rays are an integral part of fundamental and applied research in the field of solar–terrestrial physics and space weather. Cosmic ray data are applied in different fields from the discovery of high-energy particles coming to Earth from space, and new fundamental symmetries in the laws of nature, to the knowledge of residual matter and magnetic fields in interstellar space. The properties of interplanetary space are determined from intensity variations, angular distribution, and other characteristics of galactic cosmic rays. The measure of cosmic ray flux intensity variability is used as one of the significant space weather factors. The negative impact of cosmic rays is also known. The negative impact can significantly increase the level of radiation hazard and pose a threat to astronauts, crews, and passengers of high-altitude aircraft on polar routes and to modern space equipment. Therefore, methods aimed at timely detection and identification of anomalous manifestations in cosmic rays are of particular practical relevance. The article proposes a method for analyzing cosmic ray variations and detecting anomalous changes in the rate of galactic cosmic ray arrival to the Earth. The method is based on a combination of the Autoencoder neural network with wavelet transform. The use of non-linear activation functions and the ability to flexibly change the structure of the network provide the ability of the Autoencoder to approximate complex dependencies in the recorded variations of cosmic rays. The article describes the numerical operations of the method implementation. Verification of the adequacy of the neural network model is based on the use of Box–Ljung Q-statistics. On the basis of the wavelet transform constructions, data-adaptive operations for detecting complex singular structures are constructed. The parameters of the applied threshold functions are estimated with a given confidence probability based on the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-quantiles of Student’s distribution. Using data from high-latitude neutron monitor stations, it is shown that the proposed method provides efficient detection of anomalies in cosmic rays during increased solar activity and magnetic storms. Using the example of a moderate magnetic storm on 10–11 May 2019, the necessity of applying different methods and approaches to the study of cosmic ray variations is confirmed, and the importance of taking them into account when making space weather forecast is shown.https://www.mdpi.com/2073-8994/14/4/744cosmic raysspace weatherdata analysisdeep learningwavelet transform |
| spellingShingle | Oksana Mandrikova Bogdana Mandrikova Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder Symmetry cosmic rays space weather data analysis deep learning wavelet transform |
| title | Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder |
| title_full | Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder |
| title_fullStr | Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder |
| title_full_unstemmed | Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder |
| title_short | Hybrid Method for Detecting Anomalies in Cosmic ray Variations Using Neural Networks Autoencoder |
| title_sort | hybrid method for detecting anomalies in cosmic ray variations using neural networks autoencoder |
| topic | cosmic rays space weather data analysis deep learning wavelet transform |
| url | https://www.mdpi.com/2073-8994/14/4/744 |
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