EGU24-18534, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Visualizing three years of STIX X-ray flare observations using self-supervised learning

Mariia Drozdova, Vitaliy Kinakh, Francesco Ramunno, Erica Lastufka, and Slava Voloshynovskiy
Mariia Drozdova et al.
  • University of Geneva, Computer Science, Geneva, Switzerland (

Operating continuously for over three years, Solar Orbiter's STIX has observed more than 43 thousand X-ray flares. This study presents a compelling visualization of this publicly available database, using self-supervised learning to organize reconstructed flare images by their visual properties. Networks designed for self-supervised learning, such as Masked Siamese Networks or Autoencoders, are able to learn latent space embeddings which encode core characteristics of the data. We investigate the effectiveness of various pre-trained vision models, fine-tuning strategies, and image preparation. This visual representation offers a valuable starting point for identifying interesting events and grouping flares based on shared morphological characteristics, useful for conducting statistical studies or finding unique flares in this rich set of observations.

How to cite: Drozdova, M., Kinakh, V., Ramunno, F., Lastufka, E., and Voloshynovskiy, S.: Visualizing three years of STIX X-ray flare observations using self-supervised learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18534,, 2024.

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