Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
- Published
- Source
- arXiv
- Paper number
- 527
- Field
- AI / General
- arXiv ID
- 2606.28446
Key points
- C-RoPE, or Continuous Rotary Positional Embedding, uses rotary position encoding to handle irregular observation intervals.
- EANE, or Error-Aware Numeric Embedding, integrates measurement uncertainty or noise into the embedding.
- It performs multi-view self-distillation across three domain views: raw light curves, GLS periodograms, and phase-folded curves.
- On the StarEmbed benchmark, it beats hand-crafted features on 15 of 16 metrics, with macro-F1 ranging from 42.56 to 63.58.
- It is validated on downstream applications such as similarity search, zero-point drift detection, and parameter estimation.
- It reaches state of the art on 5 of the 12 non-astronomy datasets in PYRREGULAR, which highlights the importance of domain-specific design.
Paper links
External research summaries. These are not HDATF publications or measured product results.