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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17382910 |
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Table of Contents:
- <p>This presentation outlines a real-time solar radio burst detection system developed for the OVRO-LWA array. It leverages machine learning trained on physics-based synthetic data to automatically identify solar radio bursts in dynamic spectra within seconds. The system integrates fast beamformed data streaming, HDF-based data handling, and YOLO-based event detection, achieving sub-second latency for data delivery and rapid burst classification. Preliminary results demonstrate high efficiency on both simulated and observed datasets, with future improvements focusing on human-in-the-loop validation and continuous model feedback.</p>