Classifications of auroral phenomena in THEMIS All-Sky images obtained via self-supervised learning

We report a novel machine learning algorithm for automatically detecting
and classifying aurora in all-sky images (ASI) that is largely trained
without requiring ground-truth labels. By including a small number of
labeled images, we are able to automatically label all of the
approximately 700 million images in the Time History of Events and
Macroscale Interactions during Substorms (THEMIS) ASI dataset from 2008 to
2022. We use a two-stage approach. In the first stage, we adapt the Simple
framework for Contrastive Learning of Representations (SimCLR) algorithm
to learn latent representations of THEMIS all-sky images. We then finetune
a classifier  network on the latent representations our model
learns of the manually labeled Oslo aurora THEMIS (OATH) dataset. We
demonstrate that this two-stage approach achieves excellent classification
results on data for which there is no current ML classification benchmark.
The outcome of this work will facilitate efficient information retrieval
for researchers interested in specific categories of aurora and will
enable large scale statistical studies and machine learning analyses of
THEMIS all-sky images that have not previously been possible. To
demonstrate possible ways to utilize this database, we performed a
statistical analysis of the occurrence rates of auroral labels with
respect to solar wind parameters, interplanetary magnetic field vector,
and geomagnetic indices. We further investigate the occurrence rates of
auroral phenomena in the annotated data set and their geoeffectiveness by
utilizing the co-located THEMIS ground magnetometer data set.