Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
arXiv:2606.28446v1 Announce Type: cross Abstract: Light curves describe temporal variations in the brightness of celestial objects. Learning robust representations of light curves is essential for large-scale automatic discovery in the dynamic universe, but existing time-series foundation models often struggle with the uneven sampling, complex noise, and wide range of physical timescales that characterize astronomical observations. We propose a domain-informed representation learning framework for irregular astronomical time series with Joint-Embedding predictive architecture (JEPA), combining
The proliferation of advanced AI models and architectures like JEPA is enabling breakthroughs in handling complex, unevenly sampled data typical of astronomical observations, addressing long-standing challenges in scientific data processing.
Improved light-curve analysis through advanced AI means faster, more accurate discovery in dynamic astronomy, enabling better understanding of cosmic phenomena and potentially identifying novel astrophysical events.
The ability to automatically learn robust representations from challenging astronomical time series data has significantly improved, moving beyond manual feature engineering or less robust traditional time-series methods.
- · Astronomical research institutions
- · AI/ML research labs
- · Space agencies
- · Data scientists working with time-series data
- · Traditional statistical methods for time-series analysis
- · Researchers relying on labor-intensive data labeling
Further development of domain-informed AI models for other scientific fields with complex, irregular data.
Accelerated discovery of new cosmic objects and events, potentially leading to new theoretical physics insights.
Enhanced capabilities for autonomous astronomical observatories and space-based telescopes to process and prioritize data onboard.
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Read at arXiv cs.AI