A multi-scale spectral pipeline using deep learning filament detection automatically identifies 91 oscillatory events in two weeks of 2014 GONG data, recovering known events and finding new ones with periods 20-126 min.
Thurman and James R
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CVEvolve is a zero-code LLM agent harness that autonomously discovers algorithms for scientific image tasks including registration, peak detection, and segmentation, reporting improvements over baselines via iterative search and holdout evaluation.
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Automatic detection of solar filament oscillations I: Multi-scale spectral pipeline
A multi-scale spectral pipeline using deep learning filament detection automatically identifies 91 oscillatory events in two weeks of 2014 GONG data, recovering known events and finding new ones with periods 20-126 min.
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CVEvolve: Autonomous Algorithm Discovery for Unstructured Scientific Data Processing
CVEvolve is a zero-code LLM agent harness that autonomously discovers algorithms for scientific image tasks including registration, peak detection, and segmentation, reporting improvements over baselines via iterative search and holdout evaluation.