Multi-label Classification of Ground-based Auroral Emission Spectra Using 1D Vision Transformers
Jul 6, 2026·
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1 min read
Matthieu Le Lain
Abstract
Can Vision Transformer attention act as a built-in interpretability mechanism on ordered 1D scientific signals? We investigate this question through the multi-label classification of ground-based auroral emission spectra acquired in Skibotn, Norway. A 1D Vision Transformer is compared with an MLP baseline on 719 expert-annotated spectra spanning five emission classes. Both models perform strongly, and the attention maps of the ViT-1D recover known emission-line locations without any spectroscopic prior.
Date
Jul 6, 2026 — Jul 8, 2026
Location
Montpellier, France
Poster presented at the RFIAP 2026 conference (Reconnaissance des Formes, Image, Apprentissage et Perception), held jointly with CAp 2026 in Montpellier, France, July 6-8, 2026.
This work, in collaboration with the Royal Belgian Institute for Space Aeronomy and IRISA, introduces the multi-label classification of ground-based auroral emission spectra as a novel task, and shows that the attention maps of a 1D Vision Transformer align with known auroral emission lines. See the companion paper and the blog post for details.
Authors
AI for astronomy & astrophysics
PhD student at IRISA, Université Bretagne Sud (expected 2026), and lecturer in computer science,
working on foundation models for astronomy and astrophysics, with a broader interest
in deep learning applied to scientific data.
Also contributing to the UniverseTBD collaboration on vision-language models for astronomy.