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 re..., We obtained these ASI classifications using a self-supervised machine learning model. The details of the model are described in the forthcoming paper in JGR: Machine Learning and Computation., , # Classifications of auroral phenomena in THEMIS All-Sky images obtained via self-supervised learning

[https://doi.org/10.5061/dryad.sbcc2frft](https://doi.org/10.5061/dryad.sbcc2frft)

## Description of the data and file structure

This dataset contains classifications of all THEMIS All-Sky Images (ASI) captured 2008-2022 into one of six categories: `arc`, `diffuse`, `discrete`, `cloudy`, `moon`, `clear`. For each image, a probability for each of the six categories is provided. The classifications were obtained using a self-supervised machine learning model accessible at the link below.

### Files and variables

#### File: themis-asi-predictions.zip

**Description:** This compressed directory contains the classification data. The data is organized into subdirectories by date in the format `YYYY-MM-DD`. Each subdirectory contains all of the classification data for the corresponding date in compressed CSV files. Each compressed CSV file contains one hour's worth of data for one THEMIS A...