Automated bow shock and magnetopause boundary detection with Cassini using threshold and deep learning methods

Cheng, I Kit, Achilleos, Nicholas and Smith, Andy (2022) Automated bow shock and magnetopause boundary detection with Cassini using threshold and deep learning methods. Frontiers in Astronomy and Space Sciences, 9. p. 1016453. ISSN 2296-987X

fspas-09-1016453.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (4MB) | Preview
Official URL:


Two algorithms set for automatic detection of bow shock (BS) and magnetopause (MP) boundaries at Saturn using in situ magnetic field and plasma data acquired by the Cassini spacecraft are presented. Traditional threshold-based and modern deep learning algorithms were investigated for the task of boundary detection. Sections of Cassini’s orbits were pre-selected based on empirical BS and MP boundary models, and from outlier detection in magnetic field data using an autoencoder neural network. The threshold method was applied to pre-selected magnetic field and plasma data independently to compute parameters to which a threshold was applied to determine the presence of a boundary. The deep learning method used a type of convolutional neural network (CNN) called ResNet on images of magnetic field time series data and electron energy-time spectrograms to classify the presence of boundaries. 2012 data were held out of the training data to test and compare the algorithms on unseen data. The comparison showed that the CNN method applied to plasma data outperformed the threshold method. A final multiclass CNN classifier trained on plasma data obtained F1 scores of 92.1% ± 1.4% for BS crossings and 84.7% ± 1.9% for MP crossings on a corrected test dataset (from use of a bootstrap method). Reliable automated detection of boundary crossings could enable future spacecraft experiments like the PEP instrument on the upcoming JUICE spacecraft mission to dynamically adapt the best observing mode based on rapid classification of the boundary crossings as soon as it appears, thus yielding higher quality data and improved potential for scientific discovery.

Item Type: Article
Additional Information: Funding information: IC was supported by a UK STFC studentship hosted by the UCL Centre for Doctoral Training in Data Intensive Science (Grant Number ST/P006736/1). NA was supported by UK STFC Consolidated Grant Number ST/S000240/1 (UCL/MSSL-Physics and Astronomy Solar System). AS was supported by NERC grants NE/P017150/1 and NE/V002724/1. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Uncontrolled Keywords: Saturn’s magnetosphere, bow shock, magnetopause, automation, threshold anomaly detection, convolutional neural networks, explainable AI
Subjects: F500 Astronomy
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 29 Nov 2022 09:54
Last Modified: 29 Nov 2022 10:00

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics