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Classification of Solar Wind with Machine Learning
Space Physics · Submitted 6 Oct 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The new method has a median accuracy larger than $90\%$ for all categories, even using a small set of data for training.
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The abstract, sentence by sentence
We present a four-category classification algorithm for the solar wind, based on Gaussian Process. The four categories are the ones previously adopted in Xu & Borovsky [2015]: ejecta, coronal hole origin plasma, streamer belt origin plasma, and sector reversal origin plasma. The algorithm is trained and tested on a labeled portion of the OMNI dataset. It uses seven inputs: the solar wind speed $V_{sw}$, the temperature standard deviation $\sigma_T$, the sunspot number $R$, the $f_{10.7}$ index, the Alfven speed $v_A$, the proton specific entropy $S_p$ and the proton temperature $T_p$ compared to a velocity-dependent expected temperature. The output of the Gaussian Process classifier is a four element vector containing the probabilities that an event (one reading from the hourly-averaged OMNI database) belongs to each category. The probabilistic nature of the prediction allows for a more informative and flexible interpretation of the results, for instance being able to classify events as 'undecided'. The new method has a median accuracy larger than $90\%$ for all categories, even using a small set of data for training. The Receiver Operating Characteristic curve and the reliability diagram also demonstrate the excellent quality of this new method. Finally, we use the algorithm to classify a large portion of the OMNI dataset, and we present for the first time transition probabilities between different solar wind categories. Such probabilities represent the 'climatological' statistics that determine the solar wind baseline.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We present a four-category classification algorithm for the solar wind, based on Gaussian Process. | contribution 0.96 | 0.94 | 0.11 | asserted 1.00 |
| s1 | The four categories are the ones previously adopted in Xu & Borovsky [2015]: ejecta, coronal hole origin plasma, streamer belt origin plasma, and sector reversal origin plasma. | method 0.59 | 0.07 | 0.03 | asserted 0.99 |
| s2 | The algorithm is trained and tested on a labeled portion of the OMNI dataset. | method 1.00 | 0.91 | 0.02 | asserted 1.00 |
| s3 | It uses seven inputs: the solar wind speed $V_{sw}$, the temperature standard deviation $\sigma_T$, the sunspot number $R$, the $f_{10.7}$ index, the Alfven speed $v_A$, the proton specific entropy $S_p$ and the proton temperature $T_p$ compared to a velocity-dependent expected temperature. | method 1.00 | 0.78 | 0.03 | asserted 1.00 |
| s4 | The output of the Gaussian Process classifier is a four element vector containing the probabilities that an event (one reading from the hourly-averaged OMNI database) belongs to each category. | method 0.99 | 0.90 | 0.05 | asserted 1.00 |
| s5 | The probabilistic nature of the prediction allows for a more informative and flexible interpretation of the results, for instance being able to classify events as 'undecided'. | interpretation 0.70 | 0.59 | 0.06 | asserted 0.87 |
| s6 | The new method has a median accuracy larger than $90\%$ for all categories, even using a small set of data for training. | result 1.00 | 0.96 | 0.41 | asserted 1.00 |
| s7 | The Receiver Operating Characteristic curve and the reliability diagram also demonstrate the excellent quality of this new method. | result 0.97 | 0.93 | 0.14 | asserted 1.00 |
| s8 | Finally, we use the algorithm to classify a large portion of the OMNI dataset, and we present for the first time transition probabilities between different solar wind categories. | contribution 0.61 | 0.97 | 0.25 | asserted 1.00 |
| s9 | Such probabilities represent the 'climatological' statistics that determine the solar wind baseline. | interpretation 0.99 | 0.63 | 0.14 | asserted 1.00 |
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The paper
Original abstract
We present a four-category classification algorithm for the solar wind, based on Gaussian Process. The four categories are the ones previously adopted in Xu & Borovsky [2015]: ejecta, coronal hole origin plasma, streamer belt origin plasma, and sector reversal origin plasma. The algorithm is trained and tested on a labeled portion of the OMNI dataset. It uses seven inputs: the solar wind speed $V_{sw}$, the temperature standard deviation $\sigma_T$, the sunspot number $R$, the $f_{10.7}$ index, the Alfven speed $v_A$, the proton specific entropy $S_p$ and the proton temperature $T_p$ compared to a velocity-dependent expected temperature. The output of the Gaussian Process classifier is a four element vector containing the probabilities that an event (one reading from the hourly-averaged OMNI database) belongs to each category. The probabilistic nature of the prediction allows for a more informative and flexible interpretation of the results, for instance being able to classify events as 'undecided'. The new method has a median accuracy larger than $90\%$ for all categories, even using a small set of data for training. The Receiver Operating Characteristic curve and the reliability diagram also demonstrate the excellent quality of this new method. Finally, we use the algorithm to classify a large portion of the OMNI dataset, and we present for the first time transition probabilities between different solar wind categories. Such probabilities represent the 'climatological' statistics that determine the solar wind baseline.
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References
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2 arXiv papers cite this in 2 sentences
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Cites it as background 1 paper
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ML methods have also been applied to the classification of solar wind types [ref] , the nowcasting of stream interaction regions [ref] and the detection of CME structures from in-situ measurements [ref] .
Finding Novel Precursors for Solar Wind Stream Interaction Regions with Interpretable Deep Learning · Introduction · 0.99
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