Yeakel, Kiley L;
Vandegriff, Jon D;
Garton, Tadhg M;
Jackman, Caitriona M;
Clark, George;
Vines, Sarah K;
Smith, Andrew W;
(2022)
Classification of Cassini's Orbit Regions as Magnetosphere, Magnetosheath, and Solar Wind via Machine Learning.
Frontiers in Astronomy and Space Sciences
, 9
, Article 875985. 10.3389/fspas.2022.875985.
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Abstract
Several machine learning algorithms and feature subsets from a variety of particle and magnetic field instruments on-board the Cassini spacecraft were explored for their utility in classifying orbit segments as magnetosphere, magnetosheath or solar wind. Using a list of manually detected magnetopause and bow shock crossings from mission scientists, random forest (RF), support vector machine (SVM), logistic regression (LR) and recurrent neural network long short-term memory (RNN LSTM) classification algorithms were trained and tested. A detailed error analysis revealed a RNN LSTM model provided the best overall performance with a 93.1% accuracy on the unseen test set and MCC score of 0.88 when utilizing 60 min of magnetometer data (|B|, Bθ, Bϕ and BR) to predict the region at the final time step. RF models using a combination of magnetometer and particle data, spanning H+, He+, He++ and electrons at a single time step, provided a nearly equivalent performance with a test set accuracy of 91.4% and MCC score of 0.84. Derived boundary crossings from each model’s region predictions revealed that the RNN model was able to successfully detect 82.1% of labeled magnetopause crossings and 91.2% of labeled bow shock crossings, while the RF model using magnetometer and particle data detected 82.4 and 74.3%, respectively.
| Type: | Article |
|---|---|
| Title: | Classification of Cassini's Orbit Regions as Magnetosphere, Magnetosheath, and Solar Wind via Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fspas.2022.875985 |
| Publisher version: | https://doi.org/10.3389/fspas.2022.875985 |
| Language: | English |
| Additional information: | Copyright © 2022 Yeakel, Vandegriff, Garton, Jackman, Clark, Vines, Smith and Kollmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, recurrent neural network (RNN) long short-term memory (LSTM), random forest, machine learning, magnetosphere, boundary crossings, Saturn, Cassini-Huygens, EARTHS MAGNETOPAUSE |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10150638 |
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