Automatic Burst Detection in Solar Radio Spectrograms Using Deep Learning: deARCE Method
We present in detail an automatic radio-burst detection system, based on the AlexNet con- volutional neural network, for use with any kind of solar spectrogram. A full methodology for model training, performance evaluation, and feedback to the model generator has been developed with special emphasis...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/59269 |
| Acceso en línea: | http://hdl.handle.net/10017/59269 https://dx.doi.org/10.1007/s11207-023-02171-0 |
| Access Level: | acceso abierto |
| Sumario: | We present in detail an automatic radio-burst detection system, based on the AlexNet con- volutional neural network, for use with any kind of solar spectrogram. A full methodology for model training, performance evaluation, and feedback to the model generator has been developed with special emphasis on i) robustness tests against stochastic and overfitting ef- fects, ii) specific metrics adapted to the unbalanced nature of the solar-burst scenario, iii) tunable parameters for probability-threshold optimization, and iv) burst-coincidence cross match among e-Callisto stations and with external observatories (NOAA-SWPC). The re- sulting neural network configuration has been designed to accept data from observatories other than e-Callisto, either ground- or spacecraft-based. Typical False Negative and False Positive Scores in single-observatory mode are, respectively, in the 10 ? 16% and 6 ? 8% ranges, which improve further in cross-match mode. This mode includes new services ( deARCE , Xmatch ) allowing the end-user to check at a glance if a solar radio burst has taken place with a high level of confidence. |
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