Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster Data
Neural networks (NN) provide a powerful pattern recognition tool, that can be used to search large amounts of data for certain types of “events”. Our specific goal is to make use of NN in order to identify events in time series, in particular energy conversion regions (ECRs) and bursty bulk flows (B...
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Astronomy and Space Sciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fspas.2020.00051/full |
| _version_ | 1819227920273506304 |
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| author | Vlad Constantinescu Octav Marghitu |
| author_facet | Vlad Constantinescu Octav Marghitu |
| author_sort | Vlad Constantinescu |
| collection | DOAJ |
| description | Neural networks (NN) provide a powerful pattern recognition tool, that can be used to search large amounts of data for certain types of “events”. Our specific goal is to make use of NN in order to identify events in time series, in particular energy conversion regions (ECRs) and bursty bulk flows (BBFs) observed by the Cluster spacecraft in the magnetospheric tail. ECRs are regions where E·J ≠ 0 is rather well-defined and observed on time scales from a few minutes to a few tens of minutes (E is the electric field and J the current density). BBFs are high speed plasma jets, known to make a significant contribution to magnetospheric dynamics. Not surprisingly, ECRs are often associated with BBFs. The manual examination of the Cluster plasma sheet data from the summer of 2001 provided start-up sets of several ECRs and, respectively, BBFs, used to train feed-forward back-propagation NNs. Subsequently, larger volumes of Cluster data were searched for ECRs and BBFs by the trained NNs. We present the results obtained and discuss the impact of the signal-to-noise ratio on these results. |
| first_indexed | 2024-12-23T10:49:03Z |
| format | Article |
| id | doaj.art-3c3349d2444b4d3996f111873433f21b |
| institution | Directory Open Access Journal |
| issn | 2296-987X |
| language | English |
| last_indexed | 2024-12-23T10:49:03Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj.art-3c3349d2444b4d3996f111873433f21b2022-12-21T17:49:56ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2020-08-01710.3389/fspas.2020.00051548849Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster DataVlad ConstantinescuOctav MarghituNeural networks (NN) provide a powerful pattern recognition tool, that can be used to search large amounts of data for certain types of “events”. Our specific goal is to make use of NN in order to identify events in time series, in particular energy conversion regions (ECRs) and bursty bulk flows (BBFs) observed by the Cluster spacecraft in the magnetospheric tail. ECRs are regions where E·J ≠ 0 is rather well-defined and observed on time scales from a few minutes to a few tens of minutes (E is the electric field and J the current density). BBFs are high speed plasma jets, known to make a significant contribution to magnetospheric dynamics. Not surprisingly, ECRs are often associated with BBFs. The manual examination of the Cluster plasma sheet data from the summer of 2001 provided start-up sets of several ECRs and, respectively, BBFs, used to train feed-forward back-propagation NNs. Subsequently, larger volumes of Cluster data were searched for ECRs and BBFs by the trained NNs. We present the results obtained and discuss the impact of the signal-to-noise ratio on these results.https://www.frontiersin.org/article/10.3389/fspas.2020.00051/fullneural networksfeedforward backpropagationclusterenergy conversion regionbursty bulk flow |
| spellingShingle | Vlad Constantinescu Octav Marghitu Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster Data Frontiers in Astronomy and Space Sciences neural networks feedforward backpropagation cluster energy conversion region bursty bulk flow |
| title | Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster Data |
| title_full | Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster Data |
| title_fullStr | Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster Data |
| title_full_unstemmed | Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster Data |
| title_short | Neural Network Based Identification of Energy Conversion Regions and Bursty Bulk Flows in Cluster Data |
| title_sort | neural network based identification of energy conversion regions and bursty bulk flows in cluster data |
| topic | neural networks feedforward backpropagation cluster energy conversion region bursty bulk flow |
| url | https://www.frontiersin.org/article/10.3389/fspas.2020.00051/full |
| work_keys_str_mv | AT vladconstantinescu neuralnetworkbasedidentificationofenergyconversionregionsandburstybulkflowsinclusterdata AT octavmarghitu neuralnetworkbasedidentificationofenergyconversionregionsandburstybulkflowsinclusterdata |