Real-Time Solar Radio Burst Detection with Machine Learning Trained on Physics-Based Synthetic Data
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| Format: | Recurso digital |
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2025
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| _version_ | 1866901853120233472 |
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| author | Zhang, Peijin |
| author_facet | Zhang, Peijin |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17382910 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Real-Time Solar Radio Burst Detection with Machine Learning Trained on Physics-Based Synthetic Data Zhang, Peijin <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> |
| title | Real-Time Solar Radio Burst Detection with Machine Learning Trained on Physics-Based Synthetic Data |
| url | https://doi.org/10.5281/zenodo.17382910 |