Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms
Abstract Many current research efforts undertake the solar flare classification task using the Space‐weather HMI Active Region Patch (SHARP) parameters as the predictors. The SHARP parameters are scalar quantities based on spatial average or integration of physical quantities derived from the vector...
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2021SW002837 |
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| author | Hu Sun Ward Manchester IV Yang Chen |
| author_facet | Hu Sun Ward Manchester IV Yang Chen |
| author_sort | Hu Sun |
| collection | DOAJ |
| description | Abstract Many current research efforts undertake the solar flare classification task using the Space‐weather HMI Active Region Patch (SHARP) parameters as the predictors. The SHARP parameters are scalar quantities based on spatial average or integration of physical quantities derived from the vector magnetic field, which loses information of the two‐dimensional spatial distribution of the field and related quantities. In this paper, we construct two new sets of spatial features to expand the feature set used for the flare classification task. The first set uses the idea of topological data analysis to summarize the geometric information of the distributions of various SHARP quantities across active regions. The second set utilizes tools coming from spatial statistics to analyze the vertical magnetic field component Br and summarize its spatial variations and clustering patterns. All features are constructed within regions near the polarity inversion lines (PILs) and classification performances using the new features are compared against those using SHARP parameters (also along the PIL). We found that using the new features can improve the skill scores of the flare classification model and new features tend to have higher feature importance, especially the spatial statistics features. This potentially suggests that even using a single magnetic field component, Br, instead of all SHARP parameters, one can still derive strongly predictive features for flare classification. |
| format | Article |
| id | doaj-art-a9eb16c1645549cd9e13daed13308d74 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-a9eb16c1645549cd9e13daed13308d742025-01-14T16:27:22ZengWileySpace Weather1542-73902021-12-011912n/an/a10.1029/2021SW002837Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked MagnetogramsHu Sun0Ward Manchester IV1Yang Chen2Department of Statistics University of Michigan Ann Arbor MI USADepartment of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USADepartment of Statistics University of Michigan Ann Arbor MI USAAbstract Many current research efforts undertake the solar flare classification task using the Space‐weather HMI Active Region Patch (SHARP) parameters as the predictors. The SHARP parameters are scalar quantities based on spatial average or integration of physical quantities derived from the vector magnetic field, which loses information of the two‐dimensional spatial distribution of the field and related quantities. In this paper, we construct two new sets of spatial features to expand the feature set used for the flare classification task. The first set uses the idea of topological data analysis to summarize the geometric information of the distributions of various SHARP quantities across active regions. The second set utilizes tools coming from spatial statistics to analyze the vertical magnetic field component Br and summarize its spatial variations and clustering patterns. All features are constructed within regions near the polarity inversion lines (PILs) and classification performances using the new features are compared against those using SHARP parameters (also along the PIL). We found that using the new features can improve the skill scores of the flare classification model and new features tend to have higher feature importance, especially the spatial statistics features. This potentially suggests that even using a single magnetic field component, Br, instead of all SHARP parameters, one can still derive strongly predictive features for flare classification.https://doi.org/10.1029/2021SW002837 |
| spellingShingle | Hu Sun Ward Manchester IV Yang Chen Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms Space Weather |
| title | Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms |
| title_full | Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms |
| title_fullStr | Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms |
| title_full_unstemmed | Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms |
| title_short | Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms |
| title_sort | improved and interpretable solar flare predictions with spatial and topological features of the polarity inversion line masked magnetograms |
| url | https://doi.org/10.1029/2021SW002837 |
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