Automatic detection of large-scale flux ropes and their geoeffectiveness with a machine learning approach
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929490409553920 |
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| author | Pal, Sanchita Santos, Luiz F. G. dos Weiss, Andreas J. Narock, Thomas Narock, Ayris Nieves-Chinchilla, Teresa Jian, Lan K. Good, Simon W. |
| author_facet | Pal, Sanchita Santos, Luiz F. G. dos Weiss, Andreas J. Narock, Thomas Narock, Ayris Nieves-Chinchilla, Teresa Jian, Lan K. Good, Simon W. |
| contents | Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary-classification machine learning (ML) models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of auto-detected FRs. Using the properties of southward magnetic field the second model determines the geoeffectiveness of FRs. Our method identifies 88.6\% and 80\% large-scale ICMEs (duration $\ge 1$ day) observed at 1 au by Wind and Sun Earth Connection Coronal and Heliospheric Investigation (STEREO) mission, respectively. While testing with a continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during 2008 - 2014 period (recall= 0.875) but many false positives (precision= 0.56) as we do not take into account any additional solar wind properties than the magnetic properties. We found an accuracy of 0.88 when estimating the geoeffectiveness of the auto-detected FRs using our method. Thus, in space weather now-casting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and geoeffectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07798 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic detection of large-scale flux ropes and their geoeffectiveness with a machine learning approach Pal, Sanchita Santos, Luiz F. G. dos Weiss, Andreas J. Narock, Thomas Narock, Ayris Nieves-Chinchilla, Teresa Jian, Lan K. Good, Simon W. Solar and Stellar Astrophysics Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary-classification machine learning (ML) models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of auto-detected FRs. Using the properties of southward magnetic field the second model determines the geoeffectiveness of FRs. Our method identifies 88.6\% and 80\% large-scale ICMEs (duration $\ge 1$ day) observed at 1 au by Wind and Sun Earth Connection Coronal and Heliospheric Investigation (STEREO) mission, respectively. While testing with a continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during 2008 - 2014 period (recall= 0.875) but many false positives (precision= 0.56) as we do not take into account any additional solar wind properties than the magnetic properties. We found an accuracy of 0.88 when estimating the geoeffectiveness of the auto-detected FRs using our method. Thus, in space weather now-casting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and geoeffectiveness. |
| title | Automatic detection of large-scale flux ropes and their geoeffectiveness with a machine learning approach |
| topic | Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2406.07798 |