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

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.