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Machine learning analysis of Jupiter's far-ultraviolet auroral morphology

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journal contribution
posted on 15.10.2019, 08:54 by J. D. Nichols, A. Kamran, S. E. Milan
We present the first principal component analysis of Jupiter's far‐ultraviolet auroras, in order to identify the most repeatable sources of variation in the auroral morphology. We show that the most recurrent source of variance is emission just poleward of the statistical oval on the dawn side. Further significant repeatable sources of variance are localised expansions of the main emission on the dawn or dusk sides and poleward emission near noon and along the dusk side. We go on to show using a DBSCAN clustering analysis that the most significant auroral components form six repeatable auroral morphological classes. One class, exhibiting bright main and poleward dusk emissions, occurs solely during solar wind compressions. This presents an important new tool for diagnosing magnetospheric compressions at Jupiter.

Funding

This work is based on observations made with the NASA/ESA Hubble Space Telescope (program GO 14105), obtained at STScI, which is operated by AURA, Inc. for NASA. This work was supported by STFC Consolidated Grant ST/N000749/1.

History

Citation

Journal of Geophysical Research: Space Physics, 2019

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Physics and Astronomy

Version

AM (Accepted Manuscript)

Published in

Journal of Geophysical Research: Space Physics

Publisher

American Geophysical Union (AGU), Wiley

issn

0148-0227

Acceptance date

24/08/2019

Copyright date

2019

Available date

15/10/2019

Publisher version

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JA027120

Notes

HST data are available at the MAST Archive.

Language

en

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