DOI: 10.5061/dryad.sbcc2frft
title: Classifications of auroral phenomena in THEMIS All-Sky images obtained via self-supervised learning

description: 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.
description: 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.
description: # 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 ASI site. Each compressed CSV file follows the naming convention `SITE-DATETIME-probs.csv.gz`. The `DATETIME` format used for the filenames is `YYYY-MM-DDThh`, where hh represents the hour during which the data was collected and is in UTC. `SITE` is a lowercase four-letter abbreviation for the THEMIS all-sky camera site where the data was collected. A full list of THEMIS ASI sites and their abbreviations can be accessed at [https://themis.ssl.berkeley.edu/gmag/asi_list.php?selyear=4000&selmonth=13&smap=on&sinfo=on&seltxt=0](https://themis.ssl.berkeley.edu/gmag/asi_list.php?selyear=4000&selmonth=13&smap=on&sinfo=on&seltxt=0). For example, in the subdirectory named `2013-03-15`, the file `gako-20130315T08-probs.csv.gz` contains predictions for one hours' worth of data obtained from the Gakona site on March 15, 2013 beginning at 08:00 UTC and ending at 09:00 UTC. Each compressed CSV file contains 7 columns named `arc`, `diffuse`, `discrete`, `cloudy`, `moon`, `clear`, `time`, and each row contains the probabilities for each of the six classifications along with a timestamp in UTC that can be used to map the classifications to the corresponding ASI. Since THEMIS ASI are collected at a 3 second cadence, most files contain 1200 rows, though some (those overlapping daylight hours) are shorter. ## Code/software The classification data are in compressed CSV files that can be viewed using any text editor. ## Access information Other publicly accessible locations of the data: * None Data was derived from the self-supervised machine learning model that can be accessed here: * [https://doi.org/10.5281/zenodo.11397580](https://doi.org/10.5281/zenodo.11397580)