Self-Supervised Representation Learning: From Spectral Foundation Models to Auroral Emission Spectra

arXiv:2609.31206 · cs.LG, physics.space-ph · Submitted 2026-09-25 · Read on arXiv

cs.LG, physics.space-ph

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Foundation Model — 13× in this paper · explained in 3 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Linear probe — 5× in this paper · explained in 2 episodes
• AUROC — 4× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Auroral Spectrograph In Skibotn — 1× in this paper
• Computer Vision and Pattern Recognition — 1× in this paper
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• SpectraFM: Tuning into Stellar Foundation Models
• Mantis: Lightweight Foundation Model for Time Series Classification
• A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders
• Masked Transformer for Electrocardiogram Classification
• OmniSpectra: A Unified Foundation Model for Native Resolution Astronomical Spectra

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